Search Results for author: Anvar Kurmukov

Found 7 papers, 4 papers with code

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.


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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