1 citations · 2 across the 11 of their papers we have counts for
11 papers
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…
Medical Semantic Segmentation with Diffusion Pretrain
David Li, Anvar Kurmukov, Mikhail Goncharov +2
Recent advances in deep learning have shown that learning robust feature representations is critical for the success of many computer vision tasks, including medical image segmenta…
Anatomical Positional Embeddings
Mikhail Goncharov, Valentin Samokhin, Eugenia Soboleva +5
We propose a self-supervised model producing 3D anatomical positional embeddings (APE) of individual medical image voxels. APE encodes voxels' anatomical closeness, i.e., voxels of…
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…
Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation
Talgat Saparov, Anvar Kurmukov, Boris Shirokikh +1
Domain shift is one of the most salient challenges in medical computer vision. Due to immense variability in scanners' parameters and imaging protocols, even images obtained from t…
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…
Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
Ayagoz Mussabayeva, Alexey Kroshnin, Anvar Kurmukov +5
We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffe…
Connectivity-Driven Brain Parcellation via Consensus Clustering
Anvar Kurmukov, Ayagoz Mussabayeva, Yulia Denisova +2
We present two related methods for deriving connectivity-based brain atlases from individual connectomes. The proposed methods exploit a previously proposed dense connectivity repr…