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youngzhou12/
GLoRIA

pytorch

Medical Vsion-Language Pre-Training

BenchX

2410.21969

mit

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GLoRIA Checkpoint Model Card

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

Model Details

Model Type: GLoRIA
Architecture: ResNet-50 image encoder and BERT text encoder
Original Papers: GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition
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

To fine-tune GLoRIA for classification, run this command:

python bin/train.py config/classification/<dataset_name>/gloria.yml

2. Segmentation

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

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

3. Report Generation

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

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

4. Evaluation

To evaluate fine-tuned GLoRIA models, run:

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

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

Citations

bibtex

@inproceedings{huang2021gloria,
  title={GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition},
  author={Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P and Yeung, Serena},
  booktitle={Proceedings of ICCV},
  pages={3942--3951},
  year={2021}
}