Summary of Unsupervised Federated Domain Adaptation For Segmentation Of Mri Images, by Navapat Nananukul et al.
Unsupervised Federated Domain Adaptation for Segmentation of MRI Images
by Navapat Nananukul, Hamid Soltanian-zadeh, Mohammad Rostami
First submitted to arxiv on: 2 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 research paper presents a novel method for automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks, which has significant implications for clinical applications. The authors develop an unsupervised federated domain adaptation approach that leverages knowledge from multiple annotated source domains to adapt a model for effective use in an unannoted target domain. By minimizing the pair-wise distances between the distributions of the target and source domains in a latent embedding space, the method enables transfer learning without requiring persistent data annotation. The authors also demonstrate their approach on the MICCAI 2016 multi-site dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use MRI images for treating patients by automatically identifying important parts of the image. The problem is that training these models requires a lot of labeled data, which can be time-consuming and expensive to get right. The authors came up with a solution where they use lots of smaller datasets from different places to train one model that can work well in any situation. |
Keywords
» Artificial intelligence » Domain adaptation » Embedding space » Semantic segmentation » Transfer learning » Unsupervised
