Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Image synthesis is expected to provide value for the translation of machine learning
methods into clinical practice. Fundamental problems like model robustness, domain …

Abstract Domain adaptation is an important task for medical image analysis to improve
generalization on datasets collected from diverse institutes using different scanners and …

To make medical datasets accessible without sharing sensitive patient information, we
introduce a novel end-to-end approach for generative de-identification of dynamic medical …

Causally-enabled machine learning frameworks could help clinicians to identify the best
course of treatments by answering counterfactual questions. We explore this path for the …

Registering pre-operative modalities, such as magnetic resonance imaging or computed
tomography, to ultrasound images is crucial for guiding clinicians during surgeries and …

In medical image segmentation, supervised deep networks' success comes at the cost of
requiring abundant labeled data. While asking domain experts to annotate only one or a few …

Multi-modal medical image segmentation has achieved great success through supervised
deep learning networks. However, because of domain shift and limited annotation …

In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images
with important anatomical structures remains an obstacle hindering the successful …

Unsupervised domain adaptation (UDA) methods have achieved promising performance in
alleviating the domain shift between different imaging modalities. In this article, we propose …