99 citations · 108 across the 20 of their papers we have counts for
21 papers
Random Direct Preference Optimization for Radiography Report Generation
Valentin Samokhin, Boris Shirokikh, Mikhail Goncharov +5
Radiography Report Generation (RRG) has gained significant attention in medical image analysis as a promising tool for alleviating the growing workload of radiologists. However, de…
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…
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…
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…
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…
Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images
Daria Frolova, Anton Vasiliuk, Mikhail Belyaev +1
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-d…
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…
Negligible effect of brain MRI data preprocessing for tumor segmentation
Ekaterina Kondrateva, Polina Druzhinina, Alexandra Dalechina +5
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate…
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…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…
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…
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
Ivan Zakazov, Boris Shirokikh, Alexey Chernyavskiy +1
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data. We consid…
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…
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Boris Shirokikh, Ivan Zakazov, Alexey Chernyavskiy +2
MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drop…
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…
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
Egor Krivov, Valery Kostjuchenko, Alexandra Dalechina +5
Deep learning methods are actively used for brain lesion segmentation. One of the most popular models is DeepMedic, which was developed for segmentation of relatively large lesions…
Sparse Group Inductive Matrix Completion
Ivan Nazarov, Boris Shirokikh, Maria Burkina +2
We consider the problem of matrix completion with side information (\textit{inductive matrix completion}). In real-world applications many side-channel features are typically non-i…