output
20132025
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing eess.IVShow all

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eess.IV2025★ 2 cited

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study

Ashkan Moradi, Fadila Zerka, Joeran S. Bosma +7

Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (…

eess.IV2025

Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy

Vangelis Kostoulas, Arthur Guijt, Ellen M. Kerkhof +3

Brachytherapy involves bringing a radioactive source near tumor tissue using implanted needles. Image-guided brachytherapy planning requires amongst others, the reconstruction of t…