99 citations · 108 across the 20 of their papers we have counts for
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Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images
Daria Frolova, Anton Vasiliuk, Mikhail Belyaev +1
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-d…
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could…
Negligible effect of brain MRI data preprocessing for tumor segmentation
Ekaterina Kondrateva, Polina Druzhinina, Alexandra Dalechina +5
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate…
Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency
Stanislav Shimovolos, Andrey Shushko, Mikhail Belyaev +1
Deep learning methods provide significant assistance in analyzing coronavirus disease (COVID-19) in chest computed tomography (CT) images, including identification, severity assess…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…