Summary of Benchmarking Vision-language Contrastive Methods For Medical Representation Learning, by Shuvendu Roy et al.
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
by Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi, Vahid Reza Khazaie, Michael Colacci, Ali Etemad, Elham Dolatabadi, Arash Afkanpour
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 research paper benchmarks contrastive frameworks for learning multimodal representations in the medical domain, aiming to answer three questions: how transferable are general-domain representations, is multimodal training sufficient, and what’s the impact of feature granularity. Eight approaches are trained on 2.8 million image-text pairs from four datasets and evaluated on 25 downstream tasks, including classification, retrieval, and visual question-answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tested how well general representations work in the medical field, whether multimodal learning is enough, and if learning small details makes a difference. They tried eight different ways to learn from images and text together, using data from four sources and testing them on many tasks like classifying images or answering questions. |
Keywords
» Artificial intelligence » Classification » Question answering
