Summary of Complementary Information Mutual Learning For Multimodality Medical Image Segmentation, by Chuyun Shen and Wenhao Li and Haoqing Chen and Xiaoling Wang and Fengping Zhu and Yuxin Li and Xiangfeng Wang and Bo Jin
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
by Chuyun Shen, Wenhao Li, Haoqing Chen, Xiaoling Wang, Fengping Zhu, Yuxin Li, Xiangfeng Wang, Bo Jin
First submitted to arxiv on: 5 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 presents a solution to address the limitations of multimodal learning in tumor segmentation for radiologists. The existing subtraction-based joint learning methods struggle with inter-modal redundant information, leading to decreased segmentation accuracy and increased risk of overfitting. To overcome this challenge, the complementary information mutual learning (CIML) framework is proposed, which mathematically models and addresses the negative impact of inter-modal redundancy. CIML achieves this by decomposing the multimodal segmentation task into subtasks based on expert prior knowledge and introducing a message passing-based scheme to remove redundant information between modalities. The framework also incorporates complementary information learning inspired by the variational information bottleneck, which is efficiently solved using variational inference and cross-modal spatial attention. Experimental results demonstrate that CIML outperforms state-of-the-art methods in terms of validation accuracy and segmentation effect. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better diagnose tumors from different medical images. Right now, doctors have to look at many images together to get an accurate diagnosis. But this can be hard because the different images show the same things in different ways, which can confuse the doctor. To fix this problem, the researchers created a new way of combining the images called CIML (Complementary Information Mutual Learning). This method takes each image and breaks it down into smaller parts that don’t overlap with each other. Then, it lets each part learn from the others, but only if they’re showing different things. This helps the doctor get a better diagnosis by removing the confusing information. |
Keywords
» Artificial intelligence » Attention » Inference » Overfitting
