150 citations · 424 across the 27 of their papers we have counts for
9 papers · 1 filter
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…