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eess.IV2025
M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation
Boris Shirokikh, Anvar Kurmukov, Mariia Donskova +3
Domain shift presents a significant challenge in applying Deep Learning to the segmentation of 3D medical images from sources like Magnetic Resonance Imaging (MRI) and Computed Tom…
eess.IV2024
The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning
Anvar Kurmukov, Bogdan Zavolovich, Aleksandra Dalechina +2
Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural image…
eess.IV2024
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…