18 citations · 43 across the 9 of their papers we have counts for
9 papers
Suppressing Modulation Instability with Reinforcement Learning
Nikolay Kalmykov, Rishat Zagidullin, Oleg Rogov +2
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of intere…
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…
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…
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…
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…
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…