Search Results for author: Dmitry V. Dylov

Found 24 papers, 12 papers with code

Self-supervised Physics-based Denoising for Computed Tomography

no code implementations1 Nov 2022 Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov

Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods.

Computed Tomography (CT) Denoising

Medical Image Captioning via Generative Pretrained Transformers

no code implementations28 Sep 2022 Alexander Selivanov, Oleg Y. Rogov, Daniil Chesakov, Artem Shelmanov, Irina Fedulova, Dmitry V. Dylov

The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.

Image Captioning

PyTorch Image Quality: Metrics for Image Quality Assessment

1 code implementation31 Aug 2022 Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko, Dmitry V. Dylov

Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms.

Image Quality Assessment

Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging

1 code implementation31 Jul 2022 Ivan Zakazov, Vladimir Shaposhnikov, Iaroslav Bespalov, Dmitry V. Dylov

Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e. g., when the sets are produced by different hardware.

Domain Adaptation

Image Quality Assessment for Magnetic Resonance Imaging

1 code implementation15 Mar 2022 Segrey Kastryulin, Jamil Zakirov, Nicola Pezzotti, Dmitry V. Dylov

Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models.

Denoising Image Enhancement +2

DASHA: Decentralized Autofocusing System with Hierarchical Agents

1 code implementation29 Aug 2021 Anna Anikina, Oleg Y. Rogov, Dmitry V. Dylov

State-of-the-art object detection models are frequently trained offline using available datasets, such as ImageNet: large and overly diverse data that are unbalanced and hard to cluster semantically.

object-detection Object Detection

Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks

1 code implementation10 Aug 2021 Artem Razumov, Oleg Y. Rogov, Dmitry V. Dylov

To accelerate MRI, the field of compressed sensing is traditionally concerned with optimizing the image quality after a partial undersampling of the measurable $\textit{k}$-space.

Data Augmentation with Manifold Barycenters

no code implementations2 Apr 2021 Iaroslav Bespalov, Nazar Buzun, Oleg Kachan, Dmitry V. Dylov

Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original knowledge domain.

Data Augmentation

Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution

no code implementations4 Mar 2021 Aleksandr Belov, Joel Stadelmann, Sergey Kastryulin, Dmitry V. Dylov

We went below the MRI acceleration factors (a. k. a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered powerful deep learning based image enhancement methods to compensate for the underresolved images.

Image Enhancement SSIM

No-reference denoising of low-dose CT projections

no code implementations3 Feb 2021 Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov

Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients.

Denoising

Global Adaptive Filtering Layer for Computer Vision

no code implementations2 Oct 2020 Viktor Shipitsin, Iaroslav Bespalov, Dmitry V. Dylov

We devise a universal adaptive neural layer to "learn" optimal frequency filter for each image together with the weights of the base neural network that performs some computer vision task.

Denoising

Deep learning Framework for Mobile Microscopy

1 code implementation27 Jul 2020 Anatasiia Kornilova, Mikhail Salnikov, Olga Novitskaya, Maria Begicheva, Egor Sevriugov, Kirill Shcherbakov, Valeriya Pronina, Dmitry V. Dylov

Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images.

Deblurring Denoising +4

LORCK: Learnable Object-Resembling Convolution Kernels

1 code implementation9 Jul 2020 Elizaveta Lazareva, Oleg Rogov, Olga Shegai, Denis Larionov, Dmitry V. Dylov

Segmentation of certain hollow organs, such as the bladder, is especially hard to automate due to their complex geometry, vague intensity gradients in the soft tissues, and a tedious manual process of the data annotation routine.

Bladder Segmentation

Deep Negative Volume Segmentation

1 code implementation22 Jun 2020 Kristina Belikova, Oleg Rogov, Aleksandr Rybakov, Maxim V. Maslov, Dmitry V. Dylov

Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians.

BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction

no code implementations20 Jun 2020 Iaroslav Bespalov, Nazar Buzun, Dmitry V. Dylov

Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce.

Retrieval

Reinforcement Learning Framework for Deep Brain Stimulation Study

1 code implementation22 Feb 2020 Dmitrii Krylov, Remi Tachet, Romain Laroche, Michael Rosenblum, Dmitry V. Dylov

Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e. g. Parkinson's.

reinforcement-learning

Unsupervised non-parametric change point detection in quasi-periodic signals

no code implementations7 Feb 2020 Nikolay Shvetsov, Nazar Buzun, Dmitry V. Dylov

We propose a new unsupervised and non-parametric method to detect change points in intricate quasi-periodic signals.

Change Point Detection Time Series

Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

1 code implementation ECCV 2020 Valeriya Pronina, Filippos Kokkinos, Dmitry V. Dylov, Stamatios Lefkimmiatis

Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.

Deblurring Denoising +4

Reinforcement learning for suppression of collective activity in oscillatory ensembles

no code implementations25 Sep 2019 Dmitriy Krylov, Dmitry V. Dylov, Michael Rosenblum

We present a use of modern data-based machine learning approaches to suppress self-sustained collective oscillations typically signaled by ensembles of degenerative neurons in the brain.

reinforcement-learning

Deep Learning Super-Diffusion in Multiplex Networks

1 code implementation9 Nov 2018 Vito M. Leli, Saeed Osat, Timur Tlyachev, Dmitry V. Dylov, Jacob D. Biamonte

We show that modern machine learning architectures, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks with 94. 12\% accuracy.

BIG-bench Machine Learning

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