| | --- |
| | license: mit |
| | library_name: pytorch |
| | tags: |
| | - Medical Vsion-Language Pre-Training |
| | - BenchX |
| | --- |
| | |
| | # 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](https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_GLoRIA_A_Multimodal_Global-Local_Representation_Learning_Framework_for_Label-Efficient_Medical_ICCV_2021_paper.pdf) |
| | - **Benchmark Paper**: [BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays](https://arxiv.org/abs/2410.21969) |
| | - **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](https://github.com/yangzhou12/BenchX/blob/release/README.md#installation) 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} |
| | } |
| | ``` |
| | ```bibtex |
| | @inproceedings{zhou2024benchx, |
| | title={BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays}, |
| | author={Yang Zhou, Tan Li Hui Faith, Yanyu Xu, Sicong Leng, Xinxing Xu, Yong Liu, Rick Siow Mong Goh}, |
| | booktitle={Proceedings of NeurIPS}, |
| | year={2024} |
| | } |
| | ``` |
