Search Results for author: Anvar Kurmukov

Found 13 papers, 5 papers with code

M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation

no code implementations24 Feb 2025 Boris Shirokikh, Anvar Kurmukov, Mariia Donskova, Valentin Samokhin, Mikhail Belyaev, Ivan Oseledets

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 Tomography (CT).

Computed Tomography (CT) Image Segmentation +3

Medical Semantic Segmentation with Diffusion Pretrain

no code implementations31 Jan 2025 David Li, Anvar Kurmukov, Mikhail Goncharov, Roman Sokolov, Mikhail Belyaev

We introduce an auxiliary diffusion process to pretrain a model that produce generalizable feature representations, useful for a variety of downstream segmentation tasks.

Image Segmentation Linear evaluation +4

The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning

no code implementations25 Sep 2024 Anvar Kurmukov, Bogdan Zavolovich, Aleksandra Dalechina, Vladislav Proskurov, Boris Shirokikh

Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet.

Image Compression

Anatomical Positional Embeddings

1 code implementation16 Sep 2024 Mikhail Goncharov, Valentin Samokhin, Eugenia Soboleva, Roman Sokolov, Boris Shirokikh, Mikhail Belyaev, Anvar Kurmukov, Ivan Oseledets

We train our APE model on 8400 publicly available CT images of abdomen and chest regions.

Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation

no code implementations24 May 2024 Aleksei Leonov, Aleksei Zakharov, Sergey Koshelev, Maxim Pisov, Anvar Kurmukov, Mikhail Belyaev

Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes.

Segmentation

Negligible effect of brain MRI data preprocessing for tumor segmentation

1 code implementation11 Apr 2022 Ekaterina Kondrateva, Polina Druzhinina, Alexandra Dalechina, Svetlana Zolotova, Andrey Golanov, Boris Shirokikh, Mikhail Belyaev, Anvar Kurmukov

Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.

Anatomy Image Denoising +3

Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation

1 code implementation18 Jul 2021 Talgat Saparov, Anvar Kurmukov, Boris Shirokikh, Mikhail Belyaev

We analyze a dataset of paired CT images, where smooth and sharp images were reconstructed from the same sinograms with different kernels, thus providing identical anatomy but different style.

Anatomy Computed Tomography (CT) +1

CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification

no code implementations2 Jun 2020 Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov, Boris Shirokikh, Anvar Kurmukov, Ivan Blokhin, Valeria Chernina, Alexander Solovev, Victor Gombolevskiy, Sergey Morozov, Mikhail Belyaev

We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital.

Binary Classification

Connectivity-Driven Brain Parcellation via Consensus Clustering

no code implementations10 Aug 2018 Anvar Kurmukov, Ayagoz Mussabayeva, Yulia Denisova, Daniel Moyer, Boris Gutman

We present two related methods for deriving connectivity-based brain atlases from individual connectomes.

Clustering

Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms

no code implementations10 Aug 2018 Ayagoz Mussabayeva, Alexey Kroshnin, Anvar Kurmukov, Yulia Dodonova, Li Shen, Shan Cong, Lei Wang, Boris A. Gutman

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 diffeomorphisms as a kernel in a machine learning context.

General Classification Image Registration

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