DT-MIL

This repository is an official PyTorch implementation of the paper "DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image" paper from MICCAI 2021.

Citation

@inproceedings{li2021dt,
  title={DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image},
  author={Li, Hang and Yang, Fan and Zhao, Yu and Xing, Xiaohan and Zhang, Jun and Gao, Mingxuan and Huang, Junzhou and Wang, Liansheng and Yao, Jianhua},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={206--216},
  year={2021},
  organization={Springer}
}

Installation

Dependencies

• Python 3.6
• PyTorch >= 1.5.0
• einops
• numpy
• scipy
• sklearn
• openslide
• albumentations
• opencv
• efficientnet_pytorch
• yacs

Compiling

cd ./models/ops
bash ./make.sh
# unit test (should see all checking is True)
python3 test.py

Usage

Inference

EXP_DIR=<path/to/result/save/dir>

python3 -u main.py \
    --output_dir data \
    --num_input_channels 1280 \
    --num_class 2 \
    --batch_size 1 \
    --num_workers 1 \
    --num_queries 2 \
    --frozen_weights ./checkpoints/checkpoint_best.pth

Note for data

You can refer the code in script/extract_feature.py and script/merge_patch_feat.py to process your own data.

We also include sample data downloaded from TCIA CPTAC Pathology Portal for testing, which are stored in the ./data folder.

Disclaimer

This tool is for research purpose and not approved for clinical use.

This is not an official Tencent product.