MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning

• URL: http://arxiv.org/abs/2405.01583v1
• Date: Sat, 27 Apr 2024 20:03:47 GMT
• Title: MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning
• Authors: Nadia Saeed,
• Abstract summary: This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA) Our system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual learning of informative skin condition representations. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). Our system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual (English, Chinese, Spanish) learning of informative skin condition representations. Using pre-trained QA models, we further bridge the gap between visual and textual information through multimodal fusion. This approach tackles complex, open-ended questions even without predefined answer choices. We empower the generation of comprehensive answers by feeding the ViT-CLIP model with multiple responses alongside images. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery.

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