Summary of Gcs-m3vlt: Guided Context Self-attention Based Multi-modal Medical Vision Language Transformer For Retinal Image Captioning, by Teja Krishna Cherukuri et al.
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning
by Teja Krishna Cherukuri, Nagur Shareef Shaik, Jyostna Devi Bodapati, Dong Hye Ye
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 paper presents a novel vision-language model for retinal image captioning that leverages guided context self-attention to integrate visual and textual features. This approach excels in limited-supervision scenarios, capturing both local details and global clinical context. The proposed method outperforms previous Transformer-based models on the DeepEyeNet dataset, achieving a 0.023 BLEU@4 improvement and significant qualitative advancements. This model’s effectiveness in generating comprehensive medical captions has far-reaching implications for diagnosing and treating eye diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors write more accurate reports by using special AI models to analyze images of eyes. The model looks at both the tiny details in the image and the bigger picture, even when there’s not much data to work with. This makes it better than other models that struggled with this task. The results show that this new approach is really good at writing detailed reports that doctors can use to diagnose and treat eye diseases. |
Keywords
» Artificial intelligence » Bleu » Image captioning » Language model » Self attention » Transformer
