MCADS-Decoder

Rethinking Decoder Design:
Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention - Accepted in CVPR 2025

Download Paper: https://openaccess.thecvf.com/content/CVPR2025/html/Wazir_Rethinking_Decoder_Design_Improving_Biomarker_Segmentation_Using_Depth-to-Space_Restoration_and_CVPR_2025_paper.html

Please Cite it as following

@InProceedings{Wazir_2025_CVPR,
    author    = {Wazir, Saad and Kim, Daeyoung},
    title     = {Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30861-30871}
}

Setup Conda Environment

use this command to create a conda environment (all the required packages are listed in mcadsDecoder_env.yml file)

conda env create -f mcadsDecoder_env.yml

Datasets

MoNuSeg - Multi-organ nuclei segmentation from H&E stained histopathological images.

link: https://monuseg.grand-challenge.org/Data/

TNBC - Triple-negative breast cancer.

link: https://zenodo.org/records/1175282#.YMisCTZKgow

DSB - 2018 Data Science Bowl.

link: https://www.kaggle.com/c/data-science-bowl-2018/data

EM - Electron Microscopy.

link: https://www.epfl.ch/labs/cvlab/data/data-em/

Data Preprocessing

After downloading the dataset you must generate patches of images and their corresponding masks (Ground Truth), & convert it into numpy arrays or you can use dataloaders directly inside the code. Note: The last channel of masks must have black and white (0,1) values not greyscale(0 to 255) values. you can generate patches using Image_Patchyfy. Link : https://github.com/saadwazir/Image_Patchyfy

Offline Data Augmentation

(it requires albumentations library link: https://albumentations.ai)

use offline_augmentation.py to generate augmented samples

Training and Testing

1. Edit the config.txt file to set training and testing parameters and define folder paths.
2. Run the mcadsDecoder.py file in a conda environment. It contains the model, training, and testing code.

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Configurations

• Paths for training

Define paths for folders that contain patches of images and masks for training.

train_images_patch_dir=/mnt/hdd_2A/datasets/monuseg_patches_augm/images/
train_masks_patch_dir=/mnt/hdd_2A/datasets/monuseg_patches_augm/masks/

• Paths for testing

Define paths for numpy arrays that contain patches of images and masks for testing.

test_images_patch_dir=/mnt/hdd_2A/datasets/monuseg_test_patches_arrays/monuseg_org_X_test.npy
test_masks_patch_dir=/mnt/hdd_2A/datasets/monuseg_test_patches_arrays/monuseg_org_y_test.npy

Define paths for folders that contain full-size images and masks for testing.

image_full_test_directory=/mnt/hdd_2A/datasets/monuseg_org/test/image/
mask_full_test_directory=/mnt/hdd_2A/datasets/monuseg_org/test/mask/

• Training Parameters

training=False
gpu_device=0
num_epochs=200
batch_size=8
imgz_size=256

• Evaluation Parameters

Parameters for processing patches of images and masks:

patch_img_size=256
patch_step_size=128

resize_img=True #set resize_img=False if full image sizes have different width and height.
resize_height_width=1024

Parameters for processing full-size images and masks:

resize_full_images=True #if resize_full_images=False then full-size images are not scaled down, but evaluation takes more time.