1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov +6
We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and di…
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
Boris Shirokikh, Alexey Shevtsov, Anvar Kurmukov +5
Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance - positive class (lesion…
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov +8
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody…
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov +7
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation o…