150 citations · 424 across the 27 of their papers we have counts for
29 papers
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
Melaku N. Getahun, Oleg Y. Rogov, Dmitry V. Dylov +3
Retinal vascular segmentation, a widely researched topic in biomedical image processing, aims to reduce the workload of ophthalmologists in treating and detecting retinal disorders…
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
Razan Dibo, Andrey Galichin, Pavel Astashev +2
In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis. This paper presents a nov…
Self-supervised Physics-based Denoising for Computed Tomography
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. Lowering the radiation…
Medical Image Captioning via Generative Pretrained Transformers
Alexander Selivanov, Oleg Y. Rogov, Daniil Chesakov +3
The automatic clinical caption generation problem is referred to as proposed model combining the analysis of frontal chest X-Ray scans with structured patient information from the…
PyTorch Image Quality: Metrics for Image Quality Assessment
Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko +1
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algo…
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Ivan Zakazov, Vladimir Shaposhnikov, Iaroslav Bespalov +1
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…
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging
Ekaterina Kuzmina, Artem Razumov, Oleg Y. Rogov +3
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Aut…
Image Quality Assessment for Magnetic Resonance Imaging
Segrey Kastryulin, Jamil Zakirov, Nicola Pezzotti +1
Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models…
Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks
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 -space.…
DASHA: Decentralized Autofocusing System with Hierarchical Agents
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 cl…
Strong Gaussian Approximation for the Sum of Random Vectors
Nazar Buzun, Nikolay Shvetsov, Dmitry V. Dylov
This paper derives a new strong Gaussian approximation bound for the sum of independent random vectors. The approach relies on the optimal transport theory and yields \textit{expli…
Landmarks Augmentation with Manifold-Barycentric Oversampling
Iaroslav Bespalov, Nazar Buzun, Oleg Kachan +1
The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new augmentation methods to alleviate the challenge. Oftentim…
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution
Aleksandr Belov, Joel Stadelmann, Sergey Kastryulin +1
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 power…
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova +7
Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with acti…
No-reference denoising of low-dose CT projections
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. The reduction of the…
Global Adaptive Filtering Layer for Computer Vision
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 visi…
Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging
Ilyas Sirazitdinov, Heinrich Schulz, Axel Saalbach +2
Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like…
Deep learning Framework for Mobile Microscopy
Anatasiia Kornilova, Mikhail Salnikov, Olga Novitskaya +5
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.…
LORCK: Learnable Object-Resembling Convolution Kernels
Elizaveta Lazareva, Oleg Rogov, Olga Shegai +2
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 tedio…
Deep Negative Volume Segmentation
Kristina Belikova, Oleg Rogov, Aleksandr Rybakov +2
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 phys…
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders
Nina Shvetsova, Bart Bakker, Irina Fedulova +2
Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies,…
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction
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. In this work, we propose a new unsupervised approach to…
Reinforcement Learning Framework for Deep Brain Stimulation Study
Dmitrii Krylov, Remi Tachet, Romain Laroche +2
Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e.g. Parkinson's. Suppression and control of this collective syn…
Unsupervised non-parametric change point detection in quasi-periodic signals
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. The detection relies on optimal transport theory combined with…