Summary of M3t: Multi-modal Medical Transformer to Bridge Clinical Context with Visual Insights For Retinal Image Medical Description Generation, by Nagur Shareef Shaik and Teja Krishna Cherukuri and Dong Hye Ye
M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation
by Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye
First submitted to arxiv on: 19 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 The proposed Multi-Modal Medical Transformer (M3T) is a novel deep learning architecture that integrates visual representations with diagnostic keywords to generate precise and coherent medical descriptions for retinal images. This approach efficiently learns contextual information and semantics from both modalities, addressing existing challenges in automated retinal image medical description generation. Experimental studies on the DeepEyeNet dataset validate the success of M3T, demonstrating a substantial 13.5% improvement in BLEU@4 over the best-performing baseline model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help doctors diagnose and treat eye problems by generating accurate descriptions from retinal images. The existing methods have limitations, such as not considering multiple types of imaging tests or not using medical information. To overcome these challenges, the authors created M3T, a deep learning model that combines image features with medical keywords. This approach leads to more precise and meaningful descriptions for doctors. The study uses a dataset called DeepEyeNet and shows that M3T performs better than other methods. |
Keywords
* Artificial intelligence * Bleu * Deep learning * Multi modal * Semantics * Transformer
