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