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Brain MRI Screening Tool with Federated Learning

Published 23 Nov 2023 in eess.IV, cs.CV, cs.LG, and q-bio.NC | (2311.14086v1)

Abstract: 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.

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References (12)
  1. “Federated optimization: Distributed optimization beyond the datacenter,” arXiv preprint arXiv:1511.03575, 11 2015.
  2. “Federated learning of deep networks using model averaging,” ArXiv, vol. abs/1602.05629, 2 2016.
  3. “Decentralized federated learning for healthcare networks: A case study on tumor segmentation,” IEEE Access, vol. 10, pp. 8693–8708, 2022.
  4. “Federated learning enables big data for rare cancer boundary detection,” Nature communications, vol. 13, no. 1, pp. 7346, 2022.
  5. “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi, Eds. 11 2015, pp. 234–241, Springer International Publishing.
  6. “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6 2016.
  7. Pavel Iakubovskii, “Segmentation models,” https://github.com/qubvel/segmentation_models, 2019.
  8. “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature methods, vol. 18, no. 2, pp. 203–211, 2021.
  9. “Digital image processing,” 2008.
  10. CERN Knowledge Transfer, “Cafein - federated network platform for the development and deployment ai-based analysis,” https://kt.cern/kt-fund/projects/cafein-federated-network-platform-development-and-deployment-ai-based-analysis-and, 2023.
  11. “Extending nn-unet for brain tumor segmentation,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Cham, 2022, pp. 173–186, Springer International Publishing.
  12. “Winning submission to the 2021 brain tumor segmentation challenge,” https://github.com/rixez/Brats21_KAIST_MRI_Lab, 2023.
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