WSI-FT (Whole Slide Image Finetuning )

This is the official repository for our CVPR 2023 paper.

Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification

Enviroment:

Check packages in requirements.txt. Despite torch and numpy for training, openslid-python and nvidia-dali-cuda110 are quite important.

Stage-1a (baseline):

Mostly folked from CLAM, with minor modification. So just follow the docs to perform baseline, or with following steps:

1. Preparing grid patches of WSI without overlap.

bash create_patches.sh

2. Preparing pretrained patch features fow WSI head training.

bash create_feature.sh

3. Training baseline WSI model.

bash train.sh

Stage-1b (variational IB training):

bash vib_train.sh

Stage-2 (wsi-finetuning with topK):

1. Collecting top-k patches of WSI by inference vib model, save in pt form.

bash extract_topk_rois.sh

2. Perform end-to-end training.

bash e2e_train.sh

Stage-3 (training wsi head with fine-tuned patch backbone):

Now you can use finetuned patch bakcbone in stage-2 to generate patch features, then run stage-1 again with the new features.

Citation

If you find this repository useful, please consider giving a star ⭐ and citation 🦖:

@InProceedings{Li_2023_CVPR,
    author    = {Li, Honglin and Zhu, Chenglu and Zhang, Yunlong and Sun, Yuxuan and Shui, Zhongyi and Kuang, Wenwei and Zheng, Sunyi and Yang, Lin},
    title     = {Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {7454-7463}
}