ESPNet: Edge-Aware Feature Shrinkage Pyramid for Polyp Segmentation (MICCAI'25)

Architecture

Training Dataset

• You can download the training data we compiled from here: https://drive.google.com/file/d/16zqdG_3PJaRRkm2wahpE2mp-gyn8rFUF/view?usp=sharing
• You can also download the pretrained transformer here: https://drive.google.com/file/d/1OmE2vEegPPTB1JZpj2SPA6BQnXqiuD1U/view?usp=share_link
• You will need to add their paths to train.sh in scripts.
• When creating edge maps, we recommend dilating them; it produces better results.

Evaluation

• You need to add the test datasets' paths in test.py, and the model checkpoint and results paths in test.sh (or modify test.py).
• You can download the checkpoint from here: https://drive.google.com/file/d/161dyhkMQtXeF4EbykIGGMbXLBQ16Qbfp/view?usp=drive_link

Prerequisites

• Creating a virtual environment in terminal: conda create -n ESPNet python=3.8.
• Versions: torch==2.4.1 scipy==1.10.1 torchvision==0.7.0 timm==0.5.4

Results

Seen Datasets

Unseen Datasets

📄 Citation

If you use this code or find our work helpful, please cite our paper:

Raneem Toman, Venkataraman Subramanian, and Sharib Ali.
"ESPNet: Edge-Aware Feature Shrinkage Pyramid for Polyp Segmentation".
Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025.

BibTeX

@inproceedings{toman2025espnet,
  author    = {Raneem Toman and Venkataraman Subramanian and Sharib Ali},
  title     = {ESPNet: Edge-Aware Feature Shrinkage Pyramid for Polyp Segmentation},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2025},
}