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--- |
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license: mit |
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library_name: pytorch |
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tags: |
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- Medical Vsion-Language Pre-Training |
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- BenchX |
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--- |
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# GLoRIA Checkpoint Model Card |
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A retrained GLoRIA model for benchmarking medical vision-language pre-training methods within the BenchX framework. |
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## Model Details |
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- **Model Type**: GLoRIA |
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- **Architecture**: ResNet-50 image encoder and BERT text encoder |
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- **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) |
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- **Benchmark Paper**: [BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays](https://arxiv.org/abs/2410.21969) |
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- **Benchmark Framework**: https://github.com/yangzhou12/BenchX |
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## Intended Use |
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- **Primary Use Cases**: |
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- Benchmarking performance for Medical Image Classification |
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- Benchmarking performance for Medical Image Segmentation |
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- Benchmarking performance for Medical Report Generation |
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## Pre-Training Data |
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- **Dataset**: |
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- Data source(s): MIMIC-CXR |
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- Types of medical images: Frontal chest X-rays |
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- Text data type: Associated radiology reports |
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## Prerequisites |
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Please follow the [instruction](https://github.com/yangzhou12/BenchX/blob/release/README.md#installation) to install BenchX. |
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## Training & Evaluation |
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### 1. Classification |
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To fine-tune GLoRIA for classification, run this command: |
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``` |
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python bin/train.py config/classification/<dataset_name>/gloria.yml |
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``` |
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### 2. Segmentation |
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To fine-tune GLoRIA for segmentation, run this command: |
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``` |
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python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/gloria.yml |
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``` |
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### 3. Report Generation |
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To fine-tune GLoRIA for report generation, run this command: |
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``` |
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python bin/train.py config/report_generation/<dataset_name>/gloria.yml |
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``` |
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### 4. Evaluation |
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To evaluate fine-tuned GLoRIA models, run: |
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``` |
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# For classification and report generation |
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python bin/test.py config/<task_name>/<dataset_name>/gloria.yml validator.splits=[test] ckpt_dir=<path_to_checkpoint> |
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# For segmentation |
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python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/gloria.yml <path_to_checkpoint> |
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``` |
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## Citations |
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```bibtex |
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@inproceedings{huang2021gloria, |
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title={GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition}, |
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author={Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P and Yeung, Serena}, |
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booktitle={Proceedings of ICCV}, |
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pages={3942--3951}, |
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year={2021} |
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} |
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``` |
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```bibtex |
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@inproceedings{zhou2024benchx, |
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title={BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays}, |
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author={Yang Zhou, Tan Li Hui Faith, Yanyu Xu, Sicong Leng, Xinxing Xu, Yong Liu, Rick Siow Mong Goh}, |
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booktitle={Proceedings of NeurIPS}, |
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year={2024} |
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} |
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``` |
