Summary of Medifact at Mediqa-m3g 2024: Medical Question Answering in Dermatology with Multimodal Learning, by Nadia Saeed
MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning
by Nadia Saeed
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 The MEDIQA-M3G 2024 challenge requires novel solutions for multilingual and multimodal medical answer generation in dermatology. This paper addresses limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). The system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual learning of informative skin condition representations. Pre-trained QA models are used to bridge the gap between visual and textual information through multimodal fusion. This approach tackles complex, open-ended questions even without predefined answer choices. The ViT-CLIP model is fed with multiple responses alongside images to generate comprehensive answers. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better systems that can answer hard medical questions in many languages. It uses a new way of learning called weakly supervised learning. This method is good for answering open-ended questions without knowing what the correct answers are beforehand. The system looks at pictures from MEDIQA-M3G and uses them to learn about different skin conditions. Then, it combines this information with text-based medical knowledge to give more complete answers. |
Keywords
» Artificial intelligence » Cnn » Question answering » Supervised » Vit
