The Current and Future State of AI Interpretation of Medical Images | New England Journal of
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Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …

Generalization capability to unseen domains is crucial for machine learning models when
deploying to real-world conditions. We investigate the challenging problem of domain …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health
crisis spreading hundreds of countries. With the continuous growth of new infections …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …

To acquire larger samples for answering complex questions in neuroscience, researchers
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …

The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Increasingly large MRI neuroimaging datasets are becoming available, including many
highly multi-site multi-scanner datasets. Combining the data from the different scanners is …