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youngzhou12/
PTUnifier

pytorch

Medical Vsion-Language Pre-Training

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PTUnifier Checkpoint Model Card

A retrained PTUnifier model for benchmarking medical vision-language pre-training methods within the BenchX framework.

Model Details

Model Type: PTUnifier
Architecture: CLIP-ViT-Base image encoder and RoBERTa-Base text encoder
Original Papers: Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
Benchmark Paper: BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
Benchmark Framework: https://github.com/yangzhou12/BenchX

Intended Use

Primary Use Cases:
- Benchmarking performance for Medical Image Classification
- Benchmarking performance for Medical Image Segmentation
- Benchmarking performance for Medical Report Generation

Pre-Training Data

Dataset:
- Data source(s): MIMIC-CXR
- Types of medical images: Frontal chest X-rays
- Text data type: Associated radiology reports

Prerequisites

Please follow the instruction to install BenchX.

Training & Evaluation

1. Classification

Fine-tuning PTUnifier for classification is not supported yet.

2. Segmentation

To fine-tune PTUnifier for segmentation, run this command:

python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/ptunifier.yml

3. Report Generation

To fine-tune PTUnifier for report generation, run this command:

python bin/train.py config/report_generation/<dataset_name>/ptunifier.yml

4. Evaluation

To evaluate fine-tuned PTUnifier models, run:

# For classification and report generation
python bin/test.py config/<task_name>/<dataset_name>/PTUnifier.yml validator.splits=[test] ckpt_dir=<path_to_checkpoint>

# For segmentation
python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/ptunifier.yml <path_to_checkpoint>

Citations

bibtex

@inproceedings{chen2023towards,
  title={Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts},
  author={Chen, Zhihong and Diao, Shizhe and Wang, Benyou and Li, Guanbin and Wan, Xiang},
  booktitle={Proceedings of ICCV},
  pages={23403--23413},
  year={2023},
}