Summary of Tm-pathvqa:90000+ Textless Multilingual Questions For Medical Visual Question Answering, by Tonmoy Rajkhowa et al.
TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering
by Tonmoy Rajkhowa, Amartya Roy Chowdhury, Sankalp Nagaonkar, Achyut Mani Tripathi
First submitted to arxiv on: 16 Jul 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 In a breakthrough that could revolutionize medical diagnostics, researchers are developing Visual Question Answering (VQA) systems that can analyze intricate medical images to aid accurate diagnoses. Current text-based VQA systems have limitations in scenarios where hands-free interaction is crucial. A speech-based VQA system may provide a better means of interaction, allowing for simultaneous task performance. To achieve this, the researchers introduced the Textless Multilingual Pathological VQA (TMPathVQA) dataset, which contains spoken questions in English, German, and French, and comprises 98,397 multilingual spoken questions and answers based on 5,004 pathological images along with 70 hours of audio. The system was benchmarked and compared using various combinations of acoustic and visual features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you need to diagnose a medical condition by analyzing X-rays or MRIs, but your hands are busy doing something else. Currently, medical professionals have to stop what they’re doing to type in questions about the image, which can slow them down. This new system lets doctors ask questions out loud while still performing their tasks. The researchers created a special dataset with spoken questions and answers based on images of different diseases, as well as hours of audio recordings. They tested this system using different combinations of features from speech and vision. |
Keywords
» Artificial intelligence » Question answering
