Titlebook: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di; Third MICCAI Worksho Shadi Albarqouni

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https://doi.org/10.1007/978-1-4684-6724-6-of-the-art performance compared to classical image processing algorithms. However, the major downside of these methods is the high computational complexity, reliance on high-performance hardware like GPUs and the inherent black-box nature of the model. In this paper, we propose quantised stand-alon

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Incremental Learning Meets Transfer Learning: Application to?Multi-site Prostate MRI Segmentationlinear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pretrained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to gener

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Data Stealing Attack on?Medical Images: Is It Safe to?Export Networks from?Data Lakes?ck then proceeds by exporting the compression decoder network together with some image codes that leads to the image reconstruction outside the data lake. We explore the feasibility of such attacks on databases of CT and MR images, showing that it is possible to obtain perceptually meaningful recons

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