mlsanityData to 14 Oct 2026

Brain MRI Screening Tool with Federated Learning

arXiv 2311.14086 ↗First posted 23 Nov 2023eess.IV cs.CV cs.LGcited by 2

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Abstract (arXiv)

In clinical practice, we often see significant delays between MRI scans and the diagnosis made by radiologists, even for severe cases. In some cases, this may be caused by the lack of additional information and clues, so even the severe cases need to wait in the queue for diagnosis. This can be avoided if there is an automatic software tool, which would supplement additional information, alerting radiologists that the particular patient may be a severe case. We are presenting an automatic brain MRI Screening Tool and we are demonstrating its capabilities for detecting tumor-like pathologies. It is the first version on the path toward a robust multi-pathology screening solution. The tool supports Federated Learning, so multiple institutions may contribute to the model without disclosing their private data.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

5 of 12 references.

References · 5

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  3. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
  4. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 84
  5. 2021Extending nn-UNet for brain tumor segmentationcited by 36

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2 papers.