7 citations · 9 across the 10 of their papers we have counts for
10 papers
Robust Curve Detection in Volumetric Medical Imaging via Attraction Field
Farukh Yaushev, Daria Nogina, Valentin Samokhin +5
Understanding body part geometry is crucial for precise medical diagnostics. Curves effectively describe anatomical structures and are widely used in medical imaging applications r…
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…
vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images
Mikhail Goncharov, Vera Soboleva, Anvar Kurmukov +2
This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations. vox2vec representations are modeled by a Feature Pyramid Net…
Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization
Alexey Zakharov, Maxim Pisov, Alim Bukharaev +4
Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologi…
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…
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
Maxim Pisov, Vladimir Kondratenko, Alexey Zakharov +4
Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by…
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection
Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina +9
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was r…
Brain Tumor Image Retrieval via Multitask Learning
Maxim Pisov, Gleb Makarchuk, Valery Kostjuchenko +3
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting…
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Gleb Makarchuk, Vladimir Kondratenko, Maxim Pisov +3
In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably,…