---
language: en
license: mit
tags:
- deep-learning
- oncology
- pathology
- image-classification
model-index:
- name: APEDIA - Angiosarcoma PD-L1 Expression Diagnostics
  results:
  - task:
      name: PD-L1 Tumor Proportion Score Prediction
      type: tps-prediction
    metrics:
      - name: Pathologist Correlation
        type: correlation
        value: 0.87
---

# APEDIA Model Weights

## Overview
This repository hosts the model weights for [APEDIA](https://github.com/Kainmueller-Lab/APEDIA) (Angiosarcoma PD-L1 Expression DIAgnostics with Deep Learning), a deep learning tool designed to assist pathologists in assessing PD-L1 expression in angiosarcoma through the calculation of the Tumor Proportion Score (TPS) from whole-slide images (WSIs).

APEDIA aims to enhance decision-making quality in the pathology of angiosarcoma by automating the detection of tumor areas and the classification of cell types based on PD-L1 expression.

## Model Checkpoints
This repository contains two critical checkpoints for the APEDIA pipeline:

- `tp_pred_model_checkpoint.pth`: This model checkpoint is responsible for detecting tumor areas in PD-L1 stained Whole-Slide Image (WSI) patches.

- `seg_model_checkpoint.pth`: This checkpoint powers the detection and classification of cell types within tumor patches, distinguishing between background, non-tumor cells, PD-L1 positive cells, and PD-L1 negative cells.
- `deepliif_latest_net_G1.pth`: Derived from the DeepLIIF library, this is the checkpoint of a generative adversarial network (GAN) designed to isolate the hematoxylin component in IHC-stained slides. For more information on DeepLIIF, visit their [GitHub repository](https://github.com/nadeemlab/DeepLIIF/tree/main).

## Usage
The model weights will be automatically downloaded by the APEDIA library when executing predictions to calculate the Tumor Proportion Score (TPS). Ensure that you have the latest version of APEDIA installed to use these models effectively.

For detailed instructions on integrating these model weights with APEDIA and running the prediction pipeline, please refer to the [APEDIA repository](https://github.com/Kainmueller-Lab/APEDIA).

## License
The model weights are available under the MIT License. For more details, see the [LICENSE](LICENSE) file in this repository.