Summary of Re-tune: Incremental Fine Tuning Of Biomedical Vision-language Models For Multi-label Chest X-ray Classification, by Marco Mistretta et al.
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification
by Marco Mistretta, Andrew D. Bagdanov
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to fine-tuning multimodal biomedical vision-language models is introduced, leveraging large language models to steer training trajectories. In this paper, RE-tune freezes backbone networks and trains simple adaptors on top of image and text encoders for multi-label chest disease diagnosis in incremental learning scenarios. The approach is evaluated in three realistic scenarios: class-incremental, label-incremental, and data-incremental. Results demonstrate that biomedical VLMs are natural continual learners, preventing catastrophic forgetting. RE-tune achieves accurate multi-label classification while prioritizing patient privacy and demonstrating exceptional computational efficiency, making it suitable for real-world healthcare settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Biomedical vision-language models can help doctors diagnose diseases by looking at X-rays and reading medical reports. This paper introduces a new way to train these models so they can learn new things without forgetting what they already know. The method uses simple computer programs to teach the models which diseases are important, and it’s really good at doing this while keeping patient information private. It also doesn’t use too much computer power, making it useful for hospitals. |
Keywords
» Artificial intelligence » Classification » Fine tuning
