Summary of Fedia: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness, by Yangyang Xiang et al.
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
by Yangyang Xiang, Nannan Wu, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan
First submitted to arxiv on: 2 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 Federated learning for medical image segmentation has gained traction, driven by privacy concerns. However, existing research assumes uniform and complete annotations across clients, which is a challenge in medical practice. Incomplete annotations can lead to incorrectly labeled pixels, undermining neural network performance. This paper introduces FedIA, a novel solution that treats incomplete annotations as noisy data. We evaluate annotation completeness using an indicator and enhance the influence of clients with comprehensive annotations while correcting incomplete ones. Our method outperforms existing solutions on two medical image segmentation datasets. The code is available at this GitHub link. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer to recognize patterns in medical images, like X-rays or MRI scans. Usually, we assume that the people helping us with this task have complete and accurate information about what’s in the pictures. But what if they don’t? What if some of the information is missing or wrong? That’s a big problem! In this paper, scientists created a new way to handle incomplete or incorrect information called FedIA. They developed a method to figure out which people have good information and which ones don’t, and then used that information to train the computer to be better at recognizing patterns in medical images. This approach worked really well on two big datasets of medical images. |
Keywords
» Artificial intelligence » Federated learning » Image segmentation » Neural network
