search

titizheng / PAMIL

Implementation of "Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification", (CVPR 2024 Highlight).
https://vilab.hit.edu.cn/projects/pamil/
12 stars 0 forks source link
cvpr24 multi-instance-learning pytorch-implementation whole-slide-image

readme

Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification

Tingting Zheng, Kui jiang, Hongxun Yao

Harbin Institute of Technology

Update

Usage

Dataset

Preprocess TCGA Dataset

We use the same configuration of data preprocessing as DSMIL.

Preprocess CAMELYON16 Dataset

We use CLAM to preprocess CAMELYON16 at 20x. For your own dataset, you can modify and run create_patches_fp_Lung.py and extract_features_fp_LungRes18Imag.py.

Preprocessed feature vector

We use preprocessing features from MMIL. More details about this file can refer DSMIL and CLAM Thanks to their wonderful works!

| Dataset | Link | |------------|:-----| | `TCGA`|[HF link](https://pan.quark.cn/s/b6c014c29528) | `CAMELYON16-Testing`|[HF link](https://pan.quark.cn/s/7339cfb8c26c) | `CAMELYON16-Training and validation`|[HF link](https://pan.quark.cn/s/27f392595e83)

Cite this work

@inproceedings{zheng2024dynamic,
    title={Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification},
    author={Zheng, Tingting and
            Jiang, Kui and
            Yao, Hongxun},
    booktitle={CVPR},
    year={2024}
}