Summary of Federated Brain Tumor Segmentation: An Extensive Benchmark, by Matthis Manthe (liris et al.
Federated brain tumor segmentation: an extensive benchmark
by Matthis Manthe, Stefan Duffner, Carole Lartizien
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a benchmarking study for federated learning algorithms in medical image analysis, specifically focusing on brain tumor segmentation. It categorizes existing schemes into global, personalized, and hybrid methods and evaluates their performance on the Federated Brain Tumor Segmentation 2022 dataset. The results show that some methods can bring slight improvements in performance and reduce bias towards dominant data distributions. Additionally, the study explores alternative ways of distributing pooled data among institutions, including IID and limited data setups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a new way to share medical image analysis data without sharing sensitive information. This paper compares different types of federated learning methods on a big brain tumor segmentation challenge. They find that some methods work better than others and can reduce bias towards certain hospitals or centers. The study also shows how the data is distributed among hospitals, which affects the results. |
Keywords
» Artificial intelligence » Federated learning
