Awesome

KEP

The official codes for "Knowledge-enhanced Visual-Language Pretraining for Computational Pathology".

Dependencies

To install Python dependencies:

pip install ./assets/timm_ctp.tar --no-deps
pip install -r requirements.txt

Easy inference

cd ./inference

python easy_inference.py

Model Checkpoints

We provide the models' checkpoints for KEP-32_OpenPath, KEP-16_OpenPath, KEP-CTP_OpenPath, KEP-32_Quilt1m, KEP-16_Quilt1m, KEP-CTP_Quilt1m, which can be downloaded from BaiduPan. The checkpoints of KEP-CTP_OpenPath can be downloaded from google drive with link: GoogleDrive.

Data

Training Dataset

1. Pathology Knowledge Tree for knowledge encoding. Based on the structure of OncoTree, we construct a pathology knowledge tree, which contains disease names/synonyms, disease definitions, histology and cytology features.

2. Navigate to OpenPath to download pathology image-text pairs of OpenPath. Note: you might need to download data from Twitter and LAION-5B

3. Navigate to Quilt1m to download pathology image-text pairs of Quilt1m.

Evaluation Dataset

1. Retrieval: ARCH.

2. Zero-shot Patch Classification: BACH, NCT-CRC-HE-100K, KatherColon, LC25000, RenalCell, SICAP, SkinCancer, WSSS4LUAD.

3. Zero-shot WSI Tumor Subtyping: TCGA-BRCA, NSCLC: TCGA-LUAD and TCGA-LUSC, TCGA-RCC. Note: The WSIs of TCGA-RCC used in this study keeps the same as MI-Zero

Pretrained Model

Pretrained model for text encoder

1. PubMedBERT

Pretrained model for visual encoder

1. CLIP-based: CLIP, BiomedCLIP, PMC-CLIP.

2. SSL-based: CTransPath, PathSSL

Training

1. Pathology Knowledge Encoding

Projecting tree-structure pathological knowledge into a latent embedding space, where the synonyms, definitions, and corresponding histological/cytological features of the same disease are pulled together while those of different diseases are pushed apart.

:warning: IMPORTANT :exclamation: Regarding the release of PathKT, we originally intended to make it public. However, we only have usage rights due to copyright restrictions and cannot share it with third parties. Please note that all pathology knowledge is derived from https://www.pathologyoutlines.com/. We encourage you to visit their website if you need related information or resources. Please make sure to review and adhere to their copyright policy.

Run the following command to perform Pathology Knowledge Encoding:

cd ./S1_knowledge_encoding

python main.py 

The main hyper-parameters are summarized in ./S1_knowledge_encoding/bert_training/params.py

2. Pathology Knowledge Enhanced Pretraining

Leverage the established knowledge encoder to guide visual-language pretraining for computational pathology.

Run the following command to perform Pathology Knowledge Enhanced Pretraining:

cd ./S2_knowledge_enhanced_pretraining

python -m path_training.main 

The main hyper-parameters of each model are summarized in ./S1_knowledge_encoding/configs

Evaluation

1. Zero-shot Patch Classification and Cross-modal Retrieval

Run the following command to perform Cross-Modal Retrieval:

cd ./S3_model_eval

python main.py 

2. Zero-shot WSI Tumor Subtyping

The code for Zero-shot WSI Tumor Subtyping, please refer to MI-Zero. Note: You might first need to segment WSI into patches by using CLAM, and then implement zeroshot classification on each patch-level images by using MI-Zero.

Citation

If you use this code for your research or project, please cite:

@misc{zhou2024knowledgeenhanced,
  title={Knowledge-enhanced Visual-Language Pretraining for Computational Pathology}, 
  author={Xiao Zhou and Xiaoman Zhang and Chaoyi Wu and Ya Zhang and Weidi Xie and Yanfeng Wang},
  year={2024},
  eprint={2404.09942},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}