309 citations · 311 across the 10 of their papers we have counts for
13 papers · 1 filter
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
Anvar Kurmukov, Valeria Chernina, Regina Gareeva +18
Interpretation of chest computed tomography (CT) is time-consuming. Previous studies have measured the time-saving effect of using a deep-learning-based aid (DLA) for CT interpreta…
Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation
Aleksei Leonov, Aleksei Zakharov, Sergey Koshelev +3
Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation…
Redesigning Out-of-Distribution Detection on 3D Medical Images
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
Detecting out-of-distribution (OOD) samples for trusted medical image segmentation remains a significant challenge. The critical issue here is the lack of a strict definition of ab…
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
Deep Learning models perform unreliably when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distributi…
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…
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…