Titlebook: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse; Third MICCAI Worksho Shadi Albarqouni

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thyroid-hormone

Francesco Alberti,Antonella Radicchiing aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multi-scale consistency loss, wh

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Gakwaya P. Isingizwe,Giuseppe T. Cirellal learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting, i.e., a decrease in performance on the data encountered earlier. This issue makes continuous training of segmentation models for

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intricacy

https://doi.org/10.1007/978-3-031-23759-1geneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel spac

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