Titlebook: Domain Adaptation and Representation Transfer; 5th MICCAI Workshop, Lisa Koch,M. Jorge Cardoso,Dong Yang Conference proceedings 2024 The Ed

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,Compositional Representation Learning for Brain Tumour Segmentation, presence or absence of the tumour (or the tumour sub-regions) in the image are constructed. Then, vMFNet models the encoded image features with von-Mises-Fisher (vMF) distributions, via learnable and compositional vMF kernels which capture information about structures in the images. We show that go

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,Realistic Data Enrichment for Robust Image Segmentation in Histopathology,egmentation of imbalanced objects within images. Therefore, we propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups by conditioning on segmentation maps. Our method can simply expand limited clinical datasets m

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,SEDA: Self-ensembling ViT with Defensive Distillation and Adversarial Training for Robust Chest X-Refensive distillation for improved robustness against adversaries. Training using adversarial examples leads to better model generalizability and improves its ability to handle perturbations. Distillation using soft probabilities introduces uncertainty and variation into the output probabilities, ma

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,Self-prompting Large Vision Models for Few-Shot Medical Image Segmentation,s decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images). Our code is available at 

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