Fix minor errors in heart tutorial.

tutorials/cardiac-hemodynamics-assessment/tutorial.ipynb

@@ -17,20 +17,13 @@
     "\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 **Cardiothoracic Abnormalities**.\n",
     "\n",
-    "**Estimated runtime:** Completing the steps in this tutorial will take approximately 8 minutes.\n",
+    "**Estimated runtime:** Completing the steps in this tutorial will take approximately 10 minutes.\n",
     "\n",
     "## Problem Formulation\n",
     "\n",
     "We will use a multimodal dataset derived from MIMIC-CXR and MIMIC-IV-ECG, which contains approximately 50K paired CXR and ECG samples. In this tutorial, we pretrain a multimodal **CardioVAE** model using \\~49K CXR-ECG pairs via a tri-stream pretraining method. We then fine-tune this pretrained CardioVAE model on a smaller subset (\\~1K paired samples) with binary labels: **Healthy** and **Cardiothoracic Abnormality**. Lastly, we demonstrate how to interpret the trained CardioVAE model on both the CXR and ECG modalities.\n",
     "\n",
-    "This notebook is based on the work of **Suvon et al. (MICCAI 2024)**, which introduced a tri-stream pretraining strategy using a **Multimodal Variational Autoencoder (VAE)** to learn both modality-shared and modality-specific representations for assessing **Pulmonary Arterial Wedge Pressure (PAWP)**—a critical indicator of cardiac hemodynamics. The resulting model, **CardioVAE**, is implemented in the [PyKale](https://github.com/pykale/pykale) library. Here, we provide a concise example of how to use this model through PyKale's APIs—from pretraining and fine-tuning to model interpretation.\n",
-    "\n",
-    "\n",
-    "## Objectives\n",
-    "\n",
-    "1. Understand the roles of CXR and ECG in evaluating cardiac and thoracic health, and the benefits of multimodal modeling with these low-cost modalities.\n",
-    "\n",
-    "2. Learn the standard PyKale workflow for pretraining, fine-tuning, and interpreting the CardioVAE model.\n"
+    "This notebook is based on the work of **Suvon et al. (MICCAI 2024)**, which introduced a tri-stream pretraining strategy using a **Multimodal Variational Autoencoder (VAE)** to learn both modality-shared and modality-specific representations for assessing **Pulmonary Arterial Wedge Pressure (PAWP)**—a critical indicator of cardiac hemodynamics. The resulting model, **CardioVAE**, is implemented in the [PyKale](https://github.com/pykale/pykale) library. Here, we provide a concise example of how to use this model through PyKale's APIs—from pretraining and fine-tuning to model interpretation."
    ],
    "cell_type": "markdown"
   },
@@ -135,8 +128,9 @@
     "\n",
     "if \"google.colab\" in str(get_ipython()):\n",
     "    sys.path.insert(0, site.getusersitepackages())\n",
-    "    !git clone --single-branch https://github.com/pykale/embc-mmai25.git\n",
-    "    %cp -r /content/embc-mmai25/tutorials/cardiac-hemodynamics-assesment/* /content/\n",
+    "    !git clone --single-branch -b main https://github.com/pykale/embc-mmai25.git\n",
+    "    %cp -r /content/embc-mmai25/tutorials/cardiac-hemodynamics-assessment/* /content/\n",
+    "\n",
     "    %rm -r /content/embc-mmai25"
    ],
    "cell_type": "code",
@@ -487,7 +481,7 @@
     "- A **classification head** (single or multi-layer MLP)  \n",
     "- A **training step** that supports standard supervised learning with cross-entropy loss\n",
     "\n",
-    "**Estimated runtime:** 1 minute with 10 epoch\n"
+    "**Estimated runtime:** 2 minute with 10 epoch\n"
    ],
    "cell_type": "markdown"
   },