Summary of Medmax: Mixed-modal Instruction Tuning For Training Biomedical Assistants, by Hritik Bansal et al.
MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants
by Hritik Bansal, Daniel Israel, Siyan Zhao, Shufan Li, Tung Nguyen, Aditya Grover
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper presents MedMax, the first large-scale multimodal biomedical instruction-tuning dataset designed for mixed-modal foundation models. The dataset addresses limitations in existing resources by providing a diverse range of tasks, including multimodal content generation, biomedical image captioning and generation, visual chatting, and report understanding. These tasks span various medical domains like radiology and histopathology. The authors fine-tune a mixed-modal foundation model on MedMax, achieving significant performance improvements over baseline models in 12 downstream biomedical visual question-answering tasks. Additionally, they introduce a unified evaluation suite for biomedical tasks, offering a robust framework for developing next-generation mixed-modal biomedical AI assistants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MedMax is a big dataset that helps machines learn to understand and work with medical images and text together. This can be useful for making decisions about patient care and predicting the effects of treatments. The dataset includes many different tasks, like generating captions for medical images or answering questions about them. The authors used this dataset to train a machine learning model that did better than other models on similar tasks. |
Keywords
» Artificial intelligence » Image captioning » Instruction tuning » Machine learning » Question answering
