diff --git a/_toc.yml b/_toc.yml
index 5e519b3..4035441 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -23,5 +23,6 @@ parts:
 - caption: Tutorials
   chapters:
   - file: tutorials/brain-disorder-diagnosis/notebook
+  - file: tutorials/cardiac-hemodynamics-assesment/notebook
   - file: tutorials/drug-target-interaction/notebook-cross-domain
   - file: tutorials/multiomics-cancer-classification/notebook
diff --git a/tutorials/cardiac-hemodynamics-assesment/experiments/finetune_base.yml b/tutorials/cardiac-hemodynamics-assesment/experiments/finetune_base.yml
new file mode 100644
index 0000000..f659e83
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/experiments/finetune_base.yml
@@ -0,0 +1,28 @@
+DATA:
+  ECG_PATH: "/content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_last_1000.pt"
+  CXR_PATH: "/content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_last_1000.pt"
+  CSV_PATH: "/content/drive/MyDrive/EMBC_workshop_data/chexpert_healthy_abnormality_subset.csv"
+  BATCH_SIZE: 32
+  NUM_WORKERS: 2
+
+MODEL:
+  LATENT_DIM: 256
+  INPUT_IMAGE_CHANNELS: 1
+  INPUT_DIM_ECG: 60000
+  NUM_LEADS: 12
+
+FT:
+  EPOCHS: 10
+  LR: 0.001
+  CKPT_PATH: "/content/drive/MyDrive/EMBC_workshop_data/CardioVAE.pth"
+  ACCELERATOR: "gpu"
+  DEVICE: "cuda"
+  KFOLDS: 5
+  SEED: 42
+
+INTERPRET:
+  SAMPLE_IDX: 101
+  ZOOM_RANGE: [3, 3.5]
+  ECG_THRESHOLD: 0.7
+  CXR_THRESHOLD: 0.7
+  SAMPLING_RATE: 500
diff --git a/tutorials/cardiac-hemodynamics-assesment/experiments/pretraining_base.yml b/tutorials/cardiac-hemodynamics-assesment/experiments/pretraining_base.yml
new file mode 100644
index 0000000..af8c015
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/experiments/pretraining_base.yml
@@ -0,0 +1,24 @@
+DATA:
+  ECG_PATH: "/content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_1000.pt"
+  CXR_PATH: "/content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_1000.pt"
+  BATCH_SIZE: 32
+  NUM_WORKERS: 2
+
+MODEL:
+  LATENT_DIM: 128
+  INPUT_DIM_ECG: 60000
+  INPUT_DIM_CXR: 1
+  NUM_LEADS: 12
+
+TRAIN:
+  EPOCHS: 10
+  LR: 0.001
+  SEED: 123
+  DEVICE: "cuda"
+  DATA_DEVICE: "cpu"
+  LAMBDA_IMAGE: 1.0
+  LAMBDA_SIGNAL: 10.0
+  SCALE_FACTOR: 0.0001
+  SAVE_PATH: "cardioVAE.pth"
+  ACCELERATOR: "gpu"
+  DEVICES: 1
diff --git a/tutorials/cardiac-hemodynamics-assesment/finetune_config.py b/tutorials/cardiac-hemodynamics-assesment/finetune_config.py
new file mode 100644
index 0000000..9ef8ee7
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/finetune_config.py
@@ -0,0 +1,50 @@
+from yacs.config import CfgNode as CN
+
+_C = CN()
+
+# Data configuration
+_C.DATA = CN()
+_C.DATA.ECG_PATH = (
+    "/content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_last_1000.pt"
+)
+_C.DATA.CXR_PATH = (
+    "/content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_last_1000.pt"
+)
+_C.DATA.CSV_PATH = (
+    "/content/drive/MyDrive/EMBC_workshop_data/chexpert_healthy_abnormality_subset.csv"
+)
+_C.DATA.BATCH_SIZE = 32
+_C.DATA.NUM_WORKERS = 2
+_C.DATA.DATA_DEVICE = "cpu"
+
+# Model configuration
+_C.MODEL = CN()
+_C.MODEL.LATENT_DIM = 256
+_C.MODEL.INPUT_IMAGE_CHANNELS = 1
+_C.MODEL.INPUT_DIM_ECG = 60000
+_C.MODEL.NUM_LEADS = 12
+
+# Fine-tuning configuration
+_C.FT = CN()
+_C.FT.EPOCHS = 15
+_C.FT.LR = 0.001
+_C.FT.HIDDEN_DIM = 128
+_C.FT.NUM_CLASSES = 2
+_C.FT.CKPT_PATH = "/content/drive/MyDrive/EMBC_workshop_data/CardioVAE.pth"
+_C.FT.ACCELERATOR = "gpu"
+_C.FT.DEVICES = 1  # This is for PyTorch Lightning's Trainer, set as int not string
+_C.FT.DEVICE = "cuda"  # For torch.device()
+_C.FT.KFOLDS = 5
+_C.FT.SEED = 42
+
+# Interpretation configuration
+_C.INTERPRET = CN()
+_C.INTERPRET.SAMPLE_IDX = 101
+_C.INTERPRET.ZOOM_RANGE = [3, 3.5]
+_C.INTERPRET.ECG_THRESHOLD = 0.7
+_C.INTERPRET.CXR_THRESHOLD = 0.7
+_C.INTERPRET.SAMPLING_RATE = 500
+
+
+def get_cfg_defaults():
+    return _C.clone()
diff --git a/tutorials/cardiac-hemodynamics-assesment/interpret.py b/tutorials/cardiac-hemodynamics-assesment/interpret.py
new file mode 100644
index 0000000..ace3cb4
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/interpret.py
@@ -0,0 +1,187 @@
+# interpret.py
+
+import torch
+import numpy as np
+import neurokit2 as nk
+from captum.attr import IntegratedGradients
+from scipy.ndimage import binary_dilation
+
+
+def multimodal_ecg_cxr_attribution(
+    last_fold_model,
+    last_val_loader,
+    sample_idx=0,
+    ecg_threshold=0.70,
+    cxr_threshold=0.7,
+    zoom_range=(3, 3.5),
+    lead_number=12,
+    sampling_rate=500,
+):
+    """
+    Computes model attributions for multimodal (ECG + CXR) input using Integrated Gradients.
+
+    This function selects a sample from the provided validation loader and computes the attributions
+    (importance scores) for both ECG and CXR modalities using Captum's Integrated Gradients.
+    It returns all relevant arrays and data needed for downstream visualization, including normalized
+    attributions, important indices, and segment data for zoomed-in views.
+
+    Parameters
+    ----------
+    last_fold_model : torch.nn.Module
+        Trained multimodal model that accepts both CXR images and ECG waveforms as input.
+    last_val_loader : DataLoader
+        PyTorch DataLoader for the validation dataset. Each batch should yield (CXR, ECG, label).
+    sample_idx : int, optional
+        Index of the sample in the validation set to interpret (default is 0).
+    ecg_threshold : float, optional
+        Threshold (0-1) to consider ECG attributions as important (default is 0.70).
+    cxr_threshold : float, optional
+        Threshold (0-1) to consider CXR attributions as important (default is 0.70).
+    zoom_range : tuple of float, optional
+        Start and end (in seconds) for zoomed ECG visualization window (default is (3, 3.5)).
+    lead_number : int, optional
+        Number of ECG leads (default is 12).
+    sampling_rate : int, optional
+        Sampling rate of the ECG waveform in Hz (default is 500).
+
+    Returns
+    -------
+    dict
+        Dictionary containing:
+            - label : int
+                True class label for the selected sample.
+            - predicted_label : int
+                Model's predicted class for the sample.
+            - predicted_probability : float
+                Probability of the predicted class.
+            - ecg_waveform_np : np.ndarray
+                1D numpy array of the processed ECG waveform.
+            - full_time : np.ndarray
+                Time axis (seconds) for the full ECG.
+            - full_length : int
+                Number of time points in the (possibly trimmed) ECG.
+            - important_indices_full : np.ndarray
+                Indices in the full ECG considered important by attribution threshold.
+            - segment_ecg_waveform : np.ndarray
+                Zoomed ECG segment.
+            - zoom_time : np.ndarray
+                Time axis (seconds) for the zoomed ECG segment.
+            - important_indices_zoom : np.ndarray
+                Important indices within the zoomed ECG segment.
+            - zoom_start_sec : float
+                Start time (seconds) of the zoomed window.
+            - zoom_end_sec : float
+                End time (seconds) of the zoomed window.
+            - xray_image_np : np.ndarray
+                CXR image as a numpy array.
+            - x_pts, y_pts : np.ndarray
+                Coordinates of important points in the CXR image (after dilation).
+            - importance_pts : np.ndarray
+                Attribution values at (x_pts, y_pts).
+            - ecg_threshold : float
+                The threshold used for ECG attributions.
+            - cxr_threshold : float
+                The threshold used for CXR attributions.
+    """
+    # Gather all batches (as in your code)
+    batches = list(last_val_loader)
+    all_xray_images, all_ecg_waveforms, all_labels = [
+        torch.cat(items) for items in zip(*batches)
+    ]
+
+    # --- Select Sample ---
+    xray_image = (
+        all_xray_images[sample_idx]
+        .unsqueeze(0)
+        .to(next(last_fold_model.parameters()).device)
+    )
+    ecg_waveform = (
+        all_ecg_waveforms[sample_idx]
+        .unsqueeze(0)
+        .to(next(last_fold_model.parameters()).device)
+    )
+    label = all_labels[sample_idx].item()
+
+    # --- ECG Preprocessing ---
+    ecg_waveform_1d = all_ecg_waveforms[sample_idx].cpu().numpy().ravel()
+    ecg_smoothed = nk.ecg_clean(ecg_waveform_1d, sampling_rate=sampling_rate)
+    ecg_smoothed_tensor = (
+        torch.tensor(ecg_smoothed.copy(), dtype=torch.float32)
+        .unsqueeze(0)
+        .unsqueeze(0)
+        .to(next(last_fold_model.parameters()).device)
+    )
+
+    # --- Prediction ---
+    last_fold_model.eval()
+    with torch.no_grad():
+        logits = last_fold_model(xray_image, ecg_waveform)
+        probabilities = torch.softmax(logits, dim=1)
+        predicted_label = torch.argmax(probabilities, dim=1).item()
+        predicted_probability = probabilities[0, predicted_label].item()
+
+    # --- Integrated Gradients ---
+    integrated_gradients = IntegratedGradients(last_fold_model)
+    xray_image.requires_grad_(True)
+    ecg_waveform.requires_grad_(True)
+    attributions, _ = integrated_gradients.attribute(
+        inputs=(xray_image, ecg_smoothed_tensor),
+        target=predicted_label,
+        return_convergence_delta=True,
+    )
+    attributions_xray = attributions[0]
+    attributions_ecg = attributions[1]
+
+    # --- ECG Attribution ---
+    attributions_ecg_np = attributions_ecg.cpu().detach().numpy().squeeze()
+    norm_attributions_ecg = (attributions_ecg_np - attributions_ecg_np.min()) / (
+        attributions_ecg_np.max() - attributions_ecg_np.min() + 1e-8
+    )
+    ecg_waveform_np = ecg_smoothed_tensor.cpu().detach().numpy().squeeze()
+    full_length = min(60000, len(ecg_waveform_np))
+    full_time = np.arange(0, full_length) / sampling_rate / lead_number
+    important_indices_full = np.where(
+        norm_attributions_ecg[:full_length] >= ecg_threshold
+    )[0]
+
+    zoom_start = int(zoom_range[0] * 6000)
+    zoom_end = int(zoom_range[1] * 6000)
+    zoom_time = np.arange(zoom_start, zoom_end) / sampling_rate / lead_number
+    segment_ecg_waveform = ecg_waveform_np[zoom_start:zoom_end]
+    segment_attributions = norm_attributions_ecg[zoom_start:zoom_end]
+    important_indices_zoom = np.where(segment_attributions >= ecg_threshold)[0]
+    zoom_start_sec = zoom_start / sampling_rate / lead_number
+    zoom_end_sec = zoom_end / sampling_rate / lead_number
+
+    # --- CXR Attribution: Points ---
+    attributions_xray_np = attributions_xray.cpu().detach().numpy().squeeze()
+    norm_attributions_xray = (attributions_xray_np - np.min(attributions_xray_np)) / (
+        np.max(attributions_xray_np) - np.min(attributions_xray_np) + 1e-8
+    )
+    xray_image_np = xray_image.cpu().detach().numpy().squeeze()
+
+    binary_mask = norm_attributions_xray >= cxr_threshold
+    dilated_mask = binary_dilation(binary_mask, iterations=1)
+    y_pts, x_pts = np.where(dilated_mask)
+    importance_pts = norm_attributions_xray[y_pts, x_pts]
+
+    return {
+        "label": label,
+        "predicted_label": predicted_label,
+        "predicted_probability": predicted_probability,
+        "ecg_waveform_np": ecg_waveform_np,
+        "full_time": full_time,
+        "full_length": full_length,
+        "important_indices_full": important_indices_full,
+        "segment_ecg_waveform": segment_ecg_waveform,
+        "zoom_time": zoom_time,
+        "important_indices_zoom": important_indices_zoom,
+        "zoom_start_sec": zoom_start_sec,
+        "zoom_end_sec": zoom_end_sec,
+        "xray_image_np": xray_image_np,
+        "x_pts": x_pts,
+        "y_pts": y_pts,
+        "importance_pts": importance_pts,
+        "ecg_threshold": ecg_threshold,
+        "cxr_threshold": cxr_threshold,
+    }
diff --git a/tutorials/cardiac-hemodynamics-assesment/notebook.ipynb b/tutorials/cardiac-hemodynamics-assesment/notebook.ipynb
new file mode 100644
index 0000000..657535d
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/notebook.ipynb
@@ -0,0 +1,1706 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  }
+ },
+ "cells": [
+  {
+   "metadata": {},
+   "source": [
+    "# Cardiac Hemodynamics Assessment\n",
+    "\n",
+    "In this tutorial, we demonstrate how to use low-cost, non-invasive modalities—**Chest X-ray (CXR)** and **12-lead Electrocardiogram (ECG)**—to assess **Pulmonary Arterial Wedge Pressure (PAWP)**, a key indicator of cardiac hemodynamics. The goal is to reduce reliance on high-cost or invasive methods such as **Cardiac MRI** and **Right Heart Catheterization (RHC)**, enabling early screening of **pulmonary hypertension (PH)** and **heart failure (HF)**.\n",
+    "\n",
+    "This notebook is based on the work of **Suvon et al. (MICCAI 2024)**, which introduced a tri-stream pre-training method using a **multimodal variational autoencoder (VAE)** to learn both modality-shared and modality-specific representations. The resulting model, **CardioVAE**, is implemented in the [PyKale](https://github.com/pykale/pykale) library. Here, we provide a concise example of how to pre-train and fine-tune this model.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### **Objectives**\n",
+    "\n",
+    "1. **Load** a preprocessed subset of 50K paired multimodal dataset from **MIMIC-CXR** and **MIMIC-IV-ECG**. Due to resource constraints in Colab, we use a reduced version of the dataset. The full preprocessing pipeline is also shared for reference.\n",
+    "\n",
+    "2. **Pre-train** the CardioVAE model using a small subset of the paired data. This step illustrates how to run the tri-stream pretraining pipeline and save the resulting model.\n",
+    "\n",
+    "3. **Load** a separate, labeled subset of paired CXR+ECG data for fine-tuning. Labels represent **healthy** versus **cardiothoracic abnormality** cases.\n",
+    "\n",
+    "4. **Fine-tune** the pre-trained CardioVAE model on the labeled subset to adapt it for a downstream classification task.\n",
+    "\n",
+    "5. **Interpret** the fine-tuned model using **Integrated Gradients** from the Captum library to identify and visualize modality-specific salient features.\n",
+    "\n",
+    "**Note:** Please make a shortcut of https://drive.google.com/drive/folders/1N7-fMWsdK-tuB76SdC-GF1njYYGx0Z-i?usp=sharing in your MyDrive."
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "As a starting point, we will install the required packages and load a set of helper functions to support the tutorial workflow. To keep the output clean and focused on interpretation, we also suppress unnecessary warnings.\n",
+    "\n",
+    "Several helper scripts are included to modularize the code and simplify the workflow. These can be inspected directly as `.py` files in the notebook’s working directory:\n",
+    "\n",
+    "- **`pretraining_config.py`**: Defines the base configuration settings for pretraining **CardioVAE**, which can be customized or overridden using external `.yml` files.\n",
+    "\n",
+    "- **`finetune_config.py`**: Defines the base configuration settings for the fine-tuning stage. These can also be overridden with `.yml` configuration files.\n",
+    "\n",
+    "- **`remap_model_parameters.py`**: The original CardioVAE model (MICCAI 2024) used a different naming convention for model parameters. Since the model has now been integrated into the **PyKale** library with a unified API, this script remaps the original pre-trained model weights to the new parameter names for compatibility.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Connect with your google drive for data laoding\n",
+    "from google.colab import drive\n",
+    "\n",
+    "drive.mount(\"/content/drive\")"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "import os\n",
+    "import site\n",
+    "import sys\n",
+    "import warnings\n",
+    "import logging\n",
+    "\n",
+    "\n",
+    "# Disable warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"\n",
+    "\n",
+    "# Suppress PyTorch Lightning logs\n",
+    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
+    "logging.getLogger(\"pytorch_lightning.utilities.rank_zero\").setLevel(logging.ERROR)\n",
+    "logging.getLogger(\"pytorch_lightning.accelerators.cuda\").setLevel(logging.ERROR)\n",
+    "\n",
+    "\n",
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    sys.path.insert(0, site.getusersitepackages())\n",
+    "    !git clone --single-branch --branch heart-tutorial https://github.com/pykale/embc-mmai25.git\n",
+    "    %cp -r /content/embc-mmai25/tutorials/cardiac-hemodynamics-assesment/* /content/\n",
+    "    %rm -r /content/embc-mmai25"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "## Packages\n",
+    "\n",
+    "The main packages required for this tutorial are:\n",
+    "\n",
+    "- **pykale**: An open-source machine learning library developed at the University of Sheffield, focused on biomedical and scientific applications. It supports multimodal learning, domain adaptation, and interpretability.\n",
+    "\n",
+    "- **wfdb**: A toolkit for reading, writing, and processing physiological signal data, especially useful for ECG waveform analysis.\n",
+    "\n",
+    "- **yacs**: A lightweight configuration management library that helps organize experimental settings in a structured, readable format.\n",
+    "\n",
+    "- **pytorch-lightning**: A high-level framework built on PyTorch that simplifies training workflows, making code cleaner and easier to scale.\n",
+    "\n",
+    "- **tabulate**: Used to print tabular data in a readable format, helpful for summarizing results or configuration parameters.\n",
+    "\n",
+    "- **captum**: A model interpretability library for PyTorch, providing tools such as Integrated Gradients to explain model predictions.\n",
+    "\n",
+    "- **neurokit2**: A user-friendly library for physiological signal processing, especially for extracting and analyzing ECG features.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Additional Notes for Colab\n",
+    "\n",
+    "Some non-critical dependencies (e.g., `torch-geometric`) may face version conflicts when installing `pykale` on Colab. These are handled manually in the installation step.  \n",
+    "#**Note:** If you're running this notebook for the first time in Colab, be sure to **restart the runtime after installation** to properly load all libraries.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "!pip uninstall --quiet -y torch torchvision torchaudio torchdata\n",
+    "\n",
+    "!pip install --quiet torch==2.3.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n",
+    "!pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-2.3.0+cu121.html\n",
+    "\n",
+    "!pip install --quiet --user \\\n",
+    "    git+https://github.com/pykale/pykale@main \\\n",
+    "    yacs==0.1.8 wfdb pytorch-lightning tabulate captum neurokit2\\\n",
+    "    && echo \"pykale,yacs and wfdb installed successfully ✅\" \\\n",
+    "    || echo \"Failed to install pykale,yacs and wfdb ❌\""
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "## Pre-training Configuration\n",
+    "\n",
+    "To reduce code clutter in the notebook, we use a dedicated configuration file—`pretraining_config.py`—which defines the default parameters for pre-training **CardioVAE**. These defaults can be easily customized by providing an external `.yml` configuration file, such as `experiments/pretraining_base.yml`.\n",
+    "\n",
+    "This setup enables clear separation between code and experiment settings, making the workflow more modular and reproducible.\n",
+    "\n",
+    "Refer to the provided configuration files for detailed customization instructions.  \n",
+    "A breakdown of each configurable option is described in the sections that follow.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "from pretraining_config import get_cfg_defaults\n",
+    "\n",
+    "cfg = get_cfg_defaults()\n",
+    "cfg.merge_from_file(\"experiments/pretraining_base.yml\")\n",
+    "print(cfg)\n",
+    "\n",
+    "# laoding"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "DATA:\n",
+      "  BATCH_SIZE: 32\n",
+      "  CXR_PATH: /content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_1000.pt\n",
+      "  ECG_PATH: /content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_1000.pt\n",
+      "  NUM_WORKERS: 2\n",
+      "MODEL:\n",
+      "  INPUT_DIM_CXR: 1\n",
+      "  INPUT_DIM_ECG: 60000\n",
+      "  LATENT_DIM: 128\n",
+      "  NUM_LEADS: 12\n",
+      "TRAIN:\n",
+      "  ACCELERATOR: gpu\n",
+      "  DATA_DEVICE: cpu\n",
+      "  DEVICE: cuda\n",
+      "  DEVICES: 1\n",
+      "  EPOCHS: 10\n",
+      "  LAMBDA_IMAGE: 1.0\n",
+      "  LAMBDA_SIGNAL: 10.0\n",
+      "  LR: 0.001\n",
+      "  SAVE_PATH: cardioVAE.pth\n",
+      "  SCALE_FACTOR: 0.0001\n",
+      "  SEED: 123\n"
+     ]
+    }
+   ],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Data Loading and Preprocessing\n",
+    "\n",
+    "Both image and signal data typically require common preprocessing steps such as resizing, normalization, tensor conversion, and interpolation before being fed into a deep learning model.\n",
+    "\n",
+    "In this tutorial, we use a **preprocessed subset (first 1000 samples)** of the paired CXR and ECG data to demonstrate the pretraining process. This helps reduce complexity and avoids the time-intensive process of loading and preprocessing the full dataset.\n",
+    "\n",
+    "However, we also provide the CSV files containing subject IDs for the entire 50K paired dataset. If you wish to pre-train the model on the full data, you can use the **PyKale API** to load samples directly from the CXR and ECG directories with preprocessing included.\n",
+    "\n",
+    "To keep this tutorial lightweight and runnable in environments like **Google Colab**, the full-data loading functionality is commented out by default but can be easily enabled.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Note:**  \n",
+    "Please ensure the shared folder **`EMBC_workshop_data`** is added as a **shortcut to your Google Drive (My Drive)** to access the required files.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# (OPTIONAL)\n",
+    "# from kale.loaddata.signal_access import load_ecg_from_folder\n",
+    "# from kale.loaddata.image_access import load_images_from_dir\n",
+    "\n",
+    "# ecg_tensor = load_ecg_from_folder(\"/data/ecg/\", \"ecg_files.csv\")\n",
+    "# cxr_tensor = load_images_from_dir(\"/data/cxr/\", \"cxr_files.csv\")"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "import torch\n",
+    "from kale.loaddata.signal_image_access import SignalImageDataset\n",
+    "from kale.utils.seed import set_seed\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "set_seed(cfg.TRAIN.SEED)\n",
+    "\n",
+    "ecg_tensor = torch.load(cfg.DATA.ECG_PATH, map_location=cfg.TRAIN.DATA_DEVICE)\n",
+    "cxr_tensor = torch.load(cfg.DATA.CXR_PATH, map_location=cfg.TRAIN.DATA_DEVICE)\n",
+    "print(\"ECG tensor shape:\", ecg_tensor.shape)\n",
+    "print(\"CXR tensor shape:\", cxr_tensor.shape)\n",
+    "# Prepare train and val datasets using your provided method\n",
+    "train_dataset, val_dataset = SignalImageDataset.prepare_data_loaders(\n",
+    "    ecg_tensor, cxr_tensor\n",
+    ")"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Multimodal pretraining"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "## Multimodal Variational Autoencoder Pretraining\n",
+    "\n",
+    "We implement a **multimodal variational autoencoder (VAE)** using **PyKale** and **PyTorch Lightning** to jointly learn representations from paired **Chest X-ray (CXR)** images and **ECG** signals.  \n",
+    "This framework enables **unsupervised pretraining** that captures the underlying relationships between these two clinically relevant modalities.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Key Steps\n",
+    "\n",
+    "#### 🔧 Model Architecture\n",
+    "We instantiate the `SignalImageVAE`, which contains **parallel encoders and decoders** for each modality. These are fused into a **shared latent space**, allowing the model to learn both modality-specific and modality-shared representations.\n",
+    "\n",
+    "#### ⚙️ Trainer Setup\n",
+    "We use the `SignalImageTriStreamVAETrainer` to manage the **multistream training process**, handling:\n",
+    "\n",
+    "- Joint and individual modality reconstructions  \n",
+    "- ELBO loss computation  \n",
+    "- Logging and evaluation\n",
+    "\n",
+    "All hyperparameters—including learning rate, batch size, latent dimension, and modality-specific loss weights—are defined in a **central configuration file** to ensure full reproducibility.\n",
+    "\n",
+    "#### 🚀 Training Execution\n",
+    "The training loop is executed using **PyTorch Lightning’s `Trainer`**, which manages:\n",
+    "\n",
+    "- Automatic GPU/CPU selection  \n",
+    "- Epoch scheduling  \n",
+    "- Checkpointing and logging  \n",
+    "\n",
+    "Model weights are saved after training for later **fine-tuning** or **evaluation**.\n",
+    "\n",
+    "#### 📁 Configuration\n",
+    "All major parameters (data paths, batch size, learning rate, number of epochs, etc.) are defined in a **YAML or Python config**.  \n",
+    "This enables reproducible and modifiable experimentation, especially useful for scaling to larger datasets or different tasks.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "> **Note:**  \n",
+    "> To run this tutorial successfully on **Google Colab** without GPU or memory errors, some hyperparameters were adjusted:  \n",
+    "> - `latent_dim` was reduced from **256** to **128**  \n",
+    "> - `max_epochs` was set to **10** for quick training  \n",
+    ">   \n",
+    "> For better performance and stable representations, we recommend training for **at least 100 epochs** with a latent dimension of **256** in a full-scale environment.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "import pytorch_lightning as pl\n",
+    "from kale.pipeline.multimodal_trainer import SignalImageTriStreamVAETrainer\n",
+    "from kale.embed.multimodal_encoder import SignalImageVAE\n",
+    "\n",
+    "model = SignalImageVAE(\n",
+    "    image_input_channels=cfg.MODEL.INPUT_DIM_CXR,\n",
+    "    signal_input_dim=cfg.MODEL.INPUT_DIM_ECG,\n",
+    "    latent_dim=cfg.MODEL.LATENT_DIM,\n",
+    ")\n",
+    "\n",
+    "# PyKale trainer instance (all from config)\n",
+    "pl_trainer = SignalImageTriStreamVAETrainer(\n",
+    "    model=model,\n",
+    "    train_dataset=train_dataset,\n",
+    "    val_dataset=val_dataset,\n",
+    "    batch_size=cfg.DATA.BATCH_SIZE,\n",
+    "    num_workers=cfg.DATA.NUM_WORKERS,\n",
+    "    lambda_image=cfg.TRAIN.LAMBDA_IMAGE,\n",
+    "    lambda_signal=cfg.TRAIN.LAMBDA_SIGNAL,\n",
+    "    lr=cfg.TRAIN.LR,\n",
+    "    annealing_epochs=cfg.TRAIN.EPOCHS,\n",
+    "    scale_factor=cfg.TRAIN.SCALE_FACTOR,\n",
+    ")\n",
+    "\n",
+    "trainer = pl.Trainer(\n",
+    "    max_epochs=cfg.TRAIN.EPOCHS,\n",
+    "    accelerator=cfg.TRAIN.ACCELERATOR,\n",
+    "    devices=cfg.TRAIN.DEVICES,\n",
+    "    log_every_n_steps=10,\n",
+    ")\n",
+    "\n",
+    "trainer.fit(pl_trainer)\n",
+    "\n",
+    "# Save model state dict\n",
+    "torch.save(model.state_dict(), cfg.TRAIN.SAVE_PATH)\n",
+    "print(f\"Saved model state dictionary to '{cfg.TRAIN.SAVE_PATH}'\")"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "## Finetuning Configuration\n",
+    "\n",
+    "To keep the notebook clean and modular, we use a dedicated configuration file—`finetune_config.py`—that defines the default parameters for fine-tuning **CardioVAE**.  \n",
+    "These defaults can be customized using an external `.yml` file, such as `experiments/finetune_base.yml`.\n",
+    "\n",
+    "This setup ensures reproducibility and flexibility across different downstream tasks and datasets.\n",
+    "\n",
+    "Please refer to the provided configuration files for detailed instructions on how to adjust parameters such as learning rate, number of epochs, loss weights, and model paths.\n",
+    "\n",
+    "A breakdown of each configurable option is provided in the following sections.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "from finetune_config import get_cfg_defaults\n",
+    "\n",
+    "cfg = get_cfg_defaults()\n",
+    "cfg.merge_from_file(\"experiments/finetune_base.yml\")\n",
+    "print(cfg)"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "DATA:\n",
+      "  BATCH_SIZE: 32\n",
+      "  CSV_PATH: /content/drive/MyDrive/EMBC_workshop_data/chexpert_healthy_abnormality_subset.csv\n",
+      "  CXR_PATH: /content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_last_1000.pt\n",
+      "  DATA_DEVICE: cpu\n",
+      "  ECG_PATH: /content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_last_1000.pt\n",
+      "  NUM_WORKERS: 2\n",
+      "FT:\n",
+      "  ACCELERATOR: gpu\n",
+      "  CKPT_PATH: /content/drive/MyDrive/EMBC_workshop_data/CardioVAE.pth\n",
+      "  DEVICE: cuda\n",
+      "  DEVICES: 1\n",
+      "  EPOCHS: 10\n",
+      "  HIDDEN_DIM: 128\n",
+      "  KFOLDS: 5\n",
+      "  LR: 0.001\n",
+      "  NUM_CLASSES: 2\n",
+      "  SEED: 42\n",
+      "INTERPRET:\n",
+      "  CXR_THRESHOLD: 0.7\n",
+      "  ECG_THRESHOLD: 0.7\n",
+      "  LEAD_NUMBER: 12\n",
+      "  SAMPLE_IDX: 101\n",
+      "  SAMPLING_RATE: 500\n",
+      "  ZOOM_RANGE: [3, 3.5]\n",
+      "MODEL:\n",
+      "  INPUT_DIM_ECG: 60000\n",
+      "  INPUT_IMAGE_CHANNELS: 1\n",
+      "  LATENT_DIM: 256\n",
+      "  NUM_LEADS: 12\n"
+     ]
+    }
+   ],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Data Loading and Preprocessing\n",
+    "\n",
+    "Similar to the pretraining stage, we prepare paired CXR and ECG data for fine-tuning.  \n",
+    "However, unlike the original fine-tuning example in **Suvon et al. (MICCAI 2024)**—which used a private in-house dataset—we use a **publicly available** small subset from **MIMIC-CXR** and **MIMIC-IV-ECG**, with binary labels:\n",
+    "\n",
+    "- **Healthy** → `0`  \n",
+    "- **Cardiothoracic Abnormality** → `1`\n",
+    "\n",
+    "This subset is carefully selected to ensure it is **not included in the original 50K samples** used during pretraining, thereby simulating a real downstream evaluation scenario.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "\n",
+    "X_ecg_tensor = torch.load(cfg.DATA.ECG_PATH, map_location=cfg.DATA.DATA_DEVICE)\n",
+    "X_image_tensor = torch.load(cfg.DATA.CXR_PATH, map_location=cfg.DATA.DATA_DEVICE)\n",
+    "df = pd.read_csv(cfg.DATA.CSV_PATH)\n",
+    "labels = torch.tensor(df[\"label\"].values, dtype=torch.long)"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "# Multimodal Fine-tuning and Evaluation\n",
+    "\n",
+    "## Cross-Validation Split\n",
+    "\n",
+    "To assess classification performance in a **robust and unbiased** manner, we use a **Stratified K-Fold (SKF)** cross-validation strategy.  \n",
+    "This ensures that each fold maintains the original class distribution (e.g., healthy vs. abnormal), which is especially important in medical datasets where class imbalance is common.\n",
+    "\n",
+    "All cross-validation hyperparameters are defined in the config for full reproducibility:\n",
+    "\n",
+    "- **`cv_strategy`**: The cross-validation method  \n",
+    "  - *Options*: `\"skf\"` (Stratified K-Fold)  \n",
+    "  - *Default*: `\"skf\"`\n",
+    "\n",
+    "- **`num_folds`**: Number of folds  \n",
+    "  - *Default*: As defined in config (e.g., `5`)\n",
+    "\n",
+    "- **`random_state`**: Seed for reproducibility  \n",
+    "  - *Default*: As defined in config (e.g., `42`)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Model Definition\n",
+    "\n",
+    "For downstream classification, we adopt a **transfer learning setup**, where a pretrained **multimodal VAE** is used as a **feature extractor**. A shallow classifier is then fine-tuned to predict binary patient status:\n",
+    "\n",
+    "- **Healthy (`0`)**  \n",
+    "- **Cardiothoracic Abnormality (`1`)**\n",
+    "\n",
+    "The classifier is implemented as a **PyTorch Lightning module**, where a lightweight classification head is added on top of the frozen or partially trainable encoder.\n",
+    "\n",
+    "All model-related hyperparameters are defined in the config for reproducibility:\n",
+    "\n",
+    "- **`feature_extractor`**: Pretrained multimodal VAE (`SignalImageVAE`)\n",
+    "- **`classifier_head`**: Single hidden layer or linear layer for binary classification\n",
+    "- **`optimizer`**: Adam optimizer (e.g., learning rate = `0.001`)\n",
+    "- **`epochs`**: Number of fine-tuning epochs (e.g., `15`)\n",
+    "- **`metrics`**: Accuracy and AUROC are reported for each fold\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Full Pipeline Overview\n",
+    "\n",
+    "The complete fine-tuning pipeline is managed using **PyTorch Lightning**, with cross-validation handled via `sklearn.model_selection.StratifiedKFold`.\n",
+    "\n",
+    "For each fold:\n",
+    "\n",
+    "1. The pretrained **CardioVAE** model is loaded.\n",
+    "2. A new classification head is initialized.\n",
+    "3. The model is trained and validated on the corresponding train/validation split.\n",
+    "\n",
+    "This setup ensures both **reproducibility** and **fair performance evaluation** across all folds.\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "from tabulate import tabulate\n",
+    "from torch.utils.data import DataLoader, TensorDataset\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pytorch_lightning as pl\n",
+    "from kale.embed.multimodal_encoder import SignalImageVAE\n",
+    "from kale.pipeline.multimodal_trainer import SignalImageFineTuningTrainer\n",
+    "from remap_model_parameters import remap_state_dict_keys\n",
+    "\n",
+    "# Cross-validation setup\n",
+    "skf = StratifiedKFold(n_splits=cfg.FT.KFOLDS, shuffle=True, random_state=cfg.FT.SEED)\n",
+    "fold_results = []\n",
+    "\n",
+    "for fold, (train_ids, val_ids) in enumerate(skf.split(np.zeros(len(labels)), labels)):\n",
+    "    print(f\"\\n--- Fold {fold+1} ---\")\n",
+    "\n",
+    "    train_dataset = TensorDataset(\n",
+    "        X_image_tensor[train_ids], X_ecg_tensor[train_ids], labels[train_ids]\n",
+    "    )\n",
+    "    val_dataset = TensorDataset(\n",
+    "        X_image_tensor[val_ids], X_ecg_tensor[val_ids], labels[val_ids]\n",
+    "    )\n",
+    "\n",
+    "    train_loader = DataLoader(\n",
+    "        train_dataset,\n",
+    "        batch_size=cfg.DATA.BATCH_SIZE,\n",
+    "        shuffle=True,\n",
+    "        num_workers=cfg.DATA.NUM_WORKERS,\n",
+    "    )\n",
+    "    val_loader = DataLoader(\n",
+    "        val_dataset,\n",
+    "        batch_size=cfg.DATA.BATCH_SIZE,\n",
+    "        shuffle=False,\n",
+    "        num_workers=cfg.DATA.NUM_WORKERS,\n",
+    "    )\n",
+    "\n",
+    "    # Load and remap checkpoint\n",
+    "    checkpoint = torch.load(cfg.FT.CKPT_PATH, map_location=cfg.FT.DEVICE)\n",
+    "    checkpoint = remap_state_dict_keys(checkpoint)\n",
+    "\n",
+    "    pretrained_mvae = SignalImageVAE(\n",
+    "        image_input_channels=cfg.MODEL.INPUT_IMAGE_CHANNELS,\n",
+    "        signal_input_dim=cfg.MODEL.INPUT_DIM_ECG,\n",
+    "        latent_dim=cfg.MODEL.LATENT_DIM,\n",
+    "    )\n",
+    "    pretrained_mvae.load_state_dict(checkpoint, strict=False)\n",
+    "    pretrained_mvae.to(cfg.FT.DEVICE)\n",
+    "    pretrained_mvae.eval()\n",
+    "\n",
+    "    model_pl = SignalImageFineTuningTrainer(\n",
+    "        pretrained_model=pretrained_mvae,\n",
+    "        num_classes=cfg.FT.NUM_CLASSES,\n",
+    "        lr=cfg.FT.LR,\n",
+    "        hidden_dim=cfg.FT.HIDDEN_DIM,\n",
+    "    )\n",
+    "\n",
+    "    trainer = pl.Trainer(\n",
+    "        max_epochs=cfg.FT.EPOCHS,\n",
+    "        accelerator=cfg.FT.ACCELERATOR,\n",
+    "        devices=cfg.FT.DEVICES,\n",
+    "        log_every_n_steps=10,\n",
+    "        enable_checkpointing=False,\n",
+    "        logger=False,\n",
+    "    )\n",
+    "\n",
+    "    trainer.fit(model_pl, train_dataloaders=train_loader, val_dataloaders=val_loader)\n",
+    "\n",
+    "    if fold == cfg.FT.KFOLDS - 1:\n",
+    "        last_fold_model = model_pl\n",
+    "        last_val_loader = val_loader\n",
+    "\n",
+    "    val_metrics = trainer.callback_metrics\n",
+    "    acc = val_metrics[\"val_acc\"].item() if \"val_acc\" in val_metrics else float(\"nan\")\n",
+    "    auc = (\n",
+    "        val_metrics[\"val_auroc\"].item() if \"val_auroc\" in val_metrics else float(\"nan\")\n",
+    "    )\n",
+    "    mcc = val_metrics[\"val_mcc\"].item() if \"val_mcc\" in val_metrics else float(\"nan\")\n",
+    "\n",
+    "    # Print metrics for this fold\n",
+    "    print(f\"Accuracy: {acc:.3f}\")\n",
+    "    print(f\"AUROC:      {auc:.3f}\")\n",
+    "    print(f\"MCC:      {mcc:.3f}\")\n",
+    "\n",
+    "    fold_results.append((acc, auc, mcc))\n",
+    "\n",
+    "# Final summary\n",
+    "accuracies, aucs, mccs = zip(*fold_results)\n",
+    "table_data = [\n",
+    "    [\"Accuracy\", f\"{np.mean(accuracies):.3f} ± {np.std(accuracies):.3f}\"],\n",
+    "    [\"AUROC\", f\"{np.mean(aucs):.3f} ± {np.std(aucs):.3f}\"],\n",
+    "    [\"MCC\", f\"{np.mean(mccs):.3f} ± {np.std(mccs):.3f}\"],\n",
+    "]\n",
+    "print(\"\\n=== Final Cross-Validation Summary ===\")\n",
+    "print(tabulate(table_data, headers=[\"Metric\", \"Mean ± STD\"], tablefmt=\"fancy_grid\"))"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "--- Fold 1 ---\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "675530fccf554f609c09eff01636c5aa"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "0682ceb20d094104af6b227fc991d9a7"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "65e5b62be28744e68ca0aba441697f76"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "14e20b4bf0104e309868fed6a2548aa7"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6ca42590afd441c68fb7d5fb37c00e7b"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "80b4b33bcafe47239b3abe61b98b8296"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "119f5c29dfcc4ca6bc5fcdf0f0de2693"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "9e933550b6884248b7219fb702c32388"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
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+      ],
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+      "--- Fold 3 ---\n"
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+      "--- Fold 4 ---\n"
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+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "437c8641824f4123a95cf61f64c83f9d"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c7f60bfbad114bb48e151289d2697a11"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c09b217d9ebb41c4b8e40389ef42f401"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3d1afc39a7ae4ccfae8f5e5ca32956b0"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy: 0.679\n",
+      "AUROC:      0.744\n",
+      "MCC:      0.351\n",
+      "\n",
+      "=== Final Cross-Validation Summary ===\n",
+      "╒══════════╤═══════════════╕\n",
+      "│ Metric   │ Mean ± STD    │\n",
+      "╞══════════╪═══════════════╡\n",
+      "│ Accuracy │ 0.714 ± 0.023 │\n",
+      "├──────────┼───────────────┤\n",
+      "│ AUROC    │ 0.766 ± 0.012 │\n",
+      "├──────────┼───────────────┤\n",
+      "│ MCC      │ 0.420 ± 0.046 │\n",
+      "╘══════════╧═══════════════╛\n"
+     ]
+    }
+   ],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "## Model Interpretability: Visualizing ECG Attribution with Integrated Gradients\n",
+    "\n",
+    "To understand which parts of the ECG signal influenced the model's decision, we use **Integrated Gradients**, a gradient-based attribution method provided by the **Captum** library.  \n",
+    "This helps identify salient regions of the ECG that contributed most to the predicted label, improving model transparency and clinical trust.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Step-by-Step Explanation\n",
+    "\n",
+    "1. **Data Preparation**  \n",
+    "   We first load a sample from the validation set and separate its CXR and ECG components. The ECG signal is smoothed using **NeuroKit2's `ecg_clean`** function to reduce noise and enhance waveform clarity.\n",
+    "\n",
+    "2. **Model Prediction**  \n",
+    "   The sample is passed through the fine-tuned model to generate predicted probabilities and the final label. The model is set to evaluation mode and moved to the appropriate device (`CPU` or `GPU`).\n",
+    "\n",
+    "3. **Attribution with Integrated Gradients**  \n",
+    "   Using Captum's `IntegratedGradients`, we compute attribution scores for both the CXR and ECG inputs.  \n",
+    "   Here, we focus on the ECG modality by extracting its corresponding attribution tensor.\n",
+    "\n",
+    "4. **Attribution Normalization**  \n",
+    "   The ECG attribution values are normalized between 0 and 1 to prepare for visualization. A fixed threshold (e.g., 0.70) is applied to highlight the most influential regions.\n",
+    "\n",
+    "5. **Visualization**  \n",
+    "   We generate two plots:\n",
+    "   - **Full ECG View**: Highlights the important regions across the entire signal (first 12 seconds).\n",
+    "   - **Zoomed-In Segment**: Focuses on a shorter interval (e.g., 3-second segment) with higher-resolution detail of salient points.\n",
+    "\n",
+    "   In both plots, red-highlighted regions correspond to time intervals with attribution values above the defined threshold, indicating strong influence on the model’s prediction.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Notes\n",
+    "\n",
+    "- The input ECG is assumed to be flattened from a 12-lead concatenated signal (e.g., 5000 samples per lead × 12 leads = 60,000 total points).\n",
+    "- Time is converted into seconds and plotted on the x-axis with respect to lead concatenation.\n",
+    "- This visualization is useful for both **clinical insight** and **debugging model behavior**.\n",
+    "- Here, we have used the fine-tuned model from the final fold of the cross-validation stage for interpretation.\n",
+    "\n"
+   ],
+   "cell_type": "markdown"
+  },
+  {
+   "metadata": {},
+   "source": [
+    "from interpret import multimodal_ecg_cxr_attribution\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Example usage (set your variables)\n",
+    "cfg = get_cfg_defaults()\n",
+    "# If you load from YAML, use your loader method here instead\n",
+    "\n",
+    "sample_idx = cfg.INTERPRET.SAMPLE_IDX\n",
+    "zoom_range = tuple(cfg.INTERPRET.ZOOM_RANGE)\n",
+    "ecg_threshold = cfg.INTERPRET.ECG_THRESHOLD\n",
+    "cxr_threshold = cfg.INTERPRET.CXR_THRESHOLD\n",
+    "lead_number = cfg.MODEL.NUM_LEADS\n",
+    "sampling_rate = cfg.INTERPRET.SAMPLING_RATE\n",
+    "\n",
+    "\n",
+    "# Run interpretation\n",
+    "result = multimodal_ecg_cxr_attribution(\n",
+    "    last_fold_model=last_fold_model,\n",
+    "    last_val_loader=last_val_loader,\n",
+    "    sample_idx=sample_idx,\n",
+    "    ecg_threshold=ecg_threshold,\n",
+    "    cxr_threshold=cxr_threshold,\n",
+    "    zoom_range=zoom_range,\n",
+    ")\n",
+    "\n",
+    "# --- Plotting (same as before) ---\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(24, 5))\n",
+    "fig.patch.set_facecolor(\"white\")\n",
+    "\n",
+    "# 1. Full ECG\n",
+    "axes[0].set_facecolor(\"white\")\n",
+    "axes[0].plot(\n",
+    "    result[\"full_time\"],\n",
+    "    result[\"ecg_waveform_np\"][: result[\"full_length\"]],\n",
+    "    color=\"black\",\n",
+    "    linewidth=1,\n",
+    "    label=\"ECG Waveform\",\n",
+    ")\n",
+    "for idx in result[\"important_indices_full\"]:\n",
+    "    stretch_start = max(0, idx - 6)\n",
+    "    stretch_end = min(result[\"full_length\"], idx + 6 + 1)\n",
+    "    axes[0].plot(\n",
+    "        result[\"full_time\"][stretch_start:stretch_end],\n",
+    "        result[\"ecg_waveform_np\"][stretch_start:stretch_end],\n",
+    "        color=\"red\",\n",
+    "        linewidth=2,\n",
+    "    )\n",
+    "axes[0].set_xlabel(\"Time (seconds)\", fontsize=\"x-large\")\n",
+    "axes[0].set_ylabel(\"Amplitude\", fontsize=\"x-large\")\n",
+    "axes[0].set_title(\"Full ECG with Important Regions\", fontsize=\"x-large\")\n",
+    "axes[0].set_xticks(np.linspace(0, 10, 11))\n",
+    "axes[0].set_xlim([0, 10])\n",
+    "axes[0].tick_params(axis=\"x\", labelsize=\"large\")\n",
+    "axes[0].tick_params(axis=\"y\", labelsize=\"large\")\n",
+    "axes[0].legend([\"ECG Waveform\", \"Important Regions\"], fontsize=\"medium\")\n",
+    "\n",
+    "# 2. Zoomed ECG\n",
+    "axes[1].set_facecolor(\"white\")\n",
+    "axes[1].plot(\n",
+    "    result[\"zoom_time\"],\n",
+    "    result[\"segment_ecg_waveform\"],\n",
+    "    color=\"black\",\n",
+    "    linewidth=3,\n",
+    "    label=\"ECG Waveform\",\n",
+    ")\n",
+    "for idx in result[\"important_indices_zoom\"]:\n",
+    "    stretch_start = max(0, idx - 6)\n",
+    "    stretch_end = min(len(result[\"segment_ecg_waveform\"]), idx + 6 + 1)\n",
+    "    axes[1].plot(\n",
+    "        result[\"zoom_time\"][stretch_start:stretch_end],\n",
+    "        result[\"segment_ecg_waveform\"][stretch_start:stretch_end],\n",
+    "        color=\"red\",\n",
+    "        linewidth=6,\n",
+    "    )\n",
+    "axes[1].set_xticks(np.linspace(result[\"zoom_start_sec\"], result[\"zoom_end_sec\"], 11))\n",
+    "axes[1].set_xlim([result[\"zoom_start_sec\"], result[\"zoom_end_sec\"]])\n",
+    "axes[1].set_yticks([])\n",
+    "axes[1].set_xlabel(\"Time (seconds)\", fontsize=\"x-large\")\n",
+    "axes[1].set_ylabel(\"\")\n",
+    "axes[1].set_title(\n",
+    "    f'Zoomed-In ECG Segment ({result[\"zoom_start_sec\"]:.2f}s – {result[\"zoom_end_sec\"]:.2f}s)',\n",
+    "    fontsize=\"x-large\",\n",
+    ")\n",
+    "\n",
+    "# 3. CXR with points (\"X\")\n",
+    "x_pts = result[\"x_pts\"]\n",
+    "y_pts = result[\"y_pts\"]\n",
+    "importance_pts = result[\"importance_pts\"]\n",
+    "cxr_thresh = result[\"cxr_threshold\"]\n",
+    "axes[2].set_facecolor(\"white\")\n",
+    "axes[2].imshow(result[\"xray_image_np\"], cmap=\"gray\", alpha=1)\n",
+    "sc = axes[2].scatter(\n",
+    "    x_pts,\n",
+    "    y_pts,\n",
+    "    c=importance_pts,\n",
+    "    cmap=\"summer\",\n",
+    "    marker=\"x\",\n",
+    "    s=150,\n",
+    "    vmin=cxr_thresh,\n",
+    "    vmax=1.0,\n",
+    "    alpha=0.8,\n",
+    "    linewidths=0.7,\n",
+    "    edgecolor=\"black\",\n",
+    ")\n",
+    "cbar = plt.colorbar(sc, ax=axes[2], fraction=0.04, pad=0.04)\n",
+    "cbar.set_label(\"Importance (0.7–1.0)\")\n",
+    "axes[2].axis(\"off\")\n",
+    "axes[2].set_title(\"CXR: Important Regions\", fontsize=\"x-large\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 2400x500 with 4 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "execution_count": null
+  }
+ ]
+}
diff --git a/tutorials/cardiac-hemodynamics-assesment/pretraining_config.py b/tutorials/cardiac-hemodynamics-assesment/pretraining_config.py
new file mode 100644
index 0000000..d70d95c
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/pretraining_config.py
@@ -0,0 +1,39 @@
+from yacs.config import CfgNode as CN
+
+_C = CN()
+
+# Data configuration
+_C.DATA = CN()
+_C.DATA.ECG_PATH = (
+    "/content/drive/MyDrive/EMBC_workshop_data/ecg_features_tensor_1000.pt"
+)
+_C.DATA.CXR_PATH = (
+    "/content/drive/MyDrive/EMBC_workshop_data/cxr_features_tensor_1000.pt"
+)
+_C.DATA.BATCH_SIZE = 128
+_C.DATA.NUM_WORKERS = 2
+
+# Model configuration
+_C.MODEL = CN()
+_C.MODEL.LATENT_DIM = 256
+_C.MODEL.INPUT_DIM_ECG = 60000
+_C.MODEL.INPUT_DIM_CXR = 1
+_C.MODEL.NUM_LEADS = 12
+
+# Training configuration
+_C.TRAIN = CN()
+_C.TRAIN.EPOCHS = 50
+_C.TRAIN.LR = 1e-3
+_C.TRAIN.SEED = 123
+_C.TRAIN.DEVICE = "cuda"
+_C.TRAIN.DATA_DEVICE = "cpu"
+_C.TRAIN.LAMBDA_IMAGE = 1.0
+_C.TRAIN.LAMBDA_SIGNAL = 10.0
+_C.TRAIN.SCALE_FACTOR = 1e-4
+_C.TRAIN.SAVE_PATH = "/content/drive/MyDrive/EMBC_workshop_data/cardioVAE.pth"
+_C.TRAIN.ACCELERATOR = "gpu"  # or "cpu"
+_C.TRAIN.DEVICES = 1  # or 2, 4, "auto", etc.
+
+
+def get_cfg_defaults():
+    return _C.clone()
diff --git a/tutorials/cardiac-hemodynamics-assesment/remap_model_parameters.py b/tutorials/cardiac-hemodynamics-assesment/remap_model_parameters.py
new file mode 100644
index 0000000..a407dfe
--- /dev/null
+++ b/tutorials/cardiac-hemodynamics-assesment/remap_model_parameters.py
@@ -0,0 +1,18 @@
+# Remap model parameters from the pre-trained checkpoints to adjust with new parameter name.
+
+
+def remap_state_dict_keys(state_dict):
+    mapping = [
+        ("ecg_encoder.", "signal_encoder."),
+        ("ecg_decoder.", "signal_decoder."),
+        ("fc_logvar", "fc_log_var"),
+        ("signal_encoder.fc_logvar.weight", "signal_encoder.fc_log_var.weight"),
+        ("signal_encoder.fc_logvar.bias", "signal_encoder.fc_log_var.bias"),
+    ]
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        for old, new in mapping:
+            if old in k:
+                k = k.replace(old, new)
+        new_state_dict[k] = v
+    return new_state_dict