Summary of A Multimodal Approach Combining Structural and Cross-domain Textual Guidance For Weakly Supervised Oct Segmentation, by Jiaqi Yang et al.
A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation
by Jiaqi Yang, Nitish Mehta, Xiaoling Hu, Chao Chen, Chia-Ling Tsai
First submitted to arxiv on: 19 Nov 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 This novel study proposes a Weakly Supervised Semantic Segmentation (WSSS) approach for accurate segmentation of Optical Coherence Tomography (OCT) images. By leveraging image-level labels and integrating structural guidance with text-driven strategies, the method generates high-quality pseudo labels, improving segmentation performance. The approach employs two processing modules that exchange raw image features and structural features from OCT images, guiding the model to identify where lesions are likely to occur. Additionally, large-scale pretrained models from cross-domain sources provide label-informed textual guidance and synthetic descriptive integration with local semantic features and consistent synthetic descriptions. The multimodal framework enhances lesion localization accuracy, demonstrating state-of-the-art performance on three OCT datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses computers to help doctors diagnose and treat eye diseases by analyzing pictures of the retina. Right now, it’s hard for computers to accurately identify certain parts of these pictures because they need a lot of information about what those parts look like. This new method helps computers learn without needing as much information by combining what they see in the picture with what people have written about similar cases. The computer can then use this combination of visual and textual information to better identify important features, leading to more accurate diagnoses. |
Keywords
» Artificial intelligence » Semantic segmentation » Supervised
