MCADS-Decoder

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{wazir2025rethinking,
  title={Rethinking decoder design: Improving biomarker segmentation using depth-to-space restoration and residual linear attention},
  author={Wazir, Saad and Kim, Daeyoung},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={30861--30871},
  year={2025},
  doi = {10.48550/arXiv.2506.18335},
  url = {https://doi.org/10.48550/arXiv.2506.18335}
}

---

Experimental Results* on the MedCAGD-Dataset-Collection

Download Dataset from Huggingface. Link: https://huggingface.co/datasets/saadwazir/MedCAGD-Dataset-Collection

TABLE 1: ACDC DATASET RESULTS (MULTI-CLASS SEMANTIC SEGMENTATION TASK)

Method	Dice ↑	IoU ↑	HD95 ↓	RV	Myo	LV
U-Net	81.56	73.41	6.9854	76.99	80.28	87.43
MCADS	84.51	76.92	5.5595	81.16	83.27	89.09

TABLE 2: SYNAPSE DATASET RESULTS (MULTI-CLASS SEMANTIC SEGMENTATION TASK)

Method	Dice ↑	IoU ↑	HD95 ↓	Aorta	GB	KL	KR	Liver	PC	SP	SM
U-Net	70.11	59.39	44.69	84.00	56.70	72.41	62.64	86.98	48.73	81.48	67.96
MCADS	85.03	81.71	11.11	90.81	86.07	86.77	83.24	87.66	83.55	85.74	76.38
Self-Prompt SAM	86.74	-	-	91.99	69.95	85.65	85.40	97.39	79.18	94.38	89.94

TABLE 3: RESULTS ON MULTIPLE DATASETS (BINARY SEMANTIC SEGMENTATION TASK)

Method	Params ↓	Flops ↓	Skin		Polyp		Fundus		Neoplasm		Cell	All
			ISIC17	ISIC18	ETIS	ColonDB	DRIVE	FIVES	BUSI	ThyroidXL	CellSeg	Avg
U-Net	34.53 M	65.53 G	83.07	86.67	76.85	83.95	71.20	75.77	74.04	71.16	71.52	77.14
MCADS	50.90 M	61.89 G	84.14	91.01	92.24	91.37	78.42	76.05	80.03	86.33	86.68	85.14
AutoSam	41.56 M	25.11 G	-	-	79.70	83.00	-	-	-	-	-	-
Medical SAM3	840.0 M	-	-	-	86.10	-	55.80	-	-	-	-	-

---

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.

---

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.


## Acknowledgement
We gratefully acknowledge the prior contributions of the research community, which have provided the foundation for our framework.