Summary of Free Form Medical Visual Question Answering in Radiology, by Abhishek Narayanan et al.
Free Form Medical Visual Question Answering in Radiology
by Abhishek Narayanan, Rushabh Musthyala, Rahul Sankar, Anirudh Prasad Nistala, Pranav Singh, Jacopo Cirrone
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses the challenge of Visual Question Answering (VQA) in the medical domain, which requires combining computer vision, natural language processing, and knowledge representation. The authors present a novel approach to effectively represent radiology images and jointly learn multimodal representations, surpassing existing methods. They innovate by augmenting the SLAKE dataset, enabling their model to respond to diverse questions beyond immediate image content. The model achieves a top-1 accuracy of 79.55% with a less complex architecture, comparable to current state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors and computers work together better. It’s about teaching computers to understand images from medical tests and answer questions based on those images. The researchers created a new way for computers to learn from these images and questions, which is much more effective than previous methods. They also made the computer program smarter by giving it more information to learn from. This new approach can be used in hospitals to help doctors make better decisions. |
Keywords
» Artificial intelligence » Natural language processing » Question answering
