Ekaterina Kondrateva

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Maximilian Zenk, Ujjwal Baid, Sarthak Pati, Akis Linardos et autres

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation …

de, us, es, ca, hk, cn, fi, pk, fr, ch, au, tr, in, gb, ie, sg (code pays fourni par la source)

Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time

Ekaterina Kondrateva, Sandzhi Barg, Mikhail D. Vasiliev

Accurate and reproducible brain morphometry from structural MRI is critical for monitoring neuroanatomical changes across time and across imaging domains. Although deep learning has accelerated segmentation workflows, scanner-induced variability and reproducibility limitations remain-especially in longitudinal and multi-site settings. In this study, we …

USING OF A COMBINATION OF HIGH-INTENSITY LASER THERAPY AND HIGH-INTENSITY MAGNETIC THERAPY FOR LOWER LIMB MUSCLE INJURIES IN PROFESSIONAL FOOTBALL PLAYERS

Pavel Pleshkov, Denis V. Kovlen, Ekaterina Kondrateva

УДК 615.83:616-001.33ПРИМЕНЕНИЕ КОМБИНАЦИИ ВЫСОКОИНТЕНСИВНОЙ ЛАЗЕРОТЕРАПИИ И ВЫСОКОИНТЕНСИВНОЙ МАГНИТОТЕРАПИИ ПРИ ТРАВМАХ МЫШЦ НИЖНИХ КОНЕЧНОСТЕЙ У ПРОФЕССИОНАЛЬНЫХ ФУТБОЛИСТОВ Плешков П.С. 1 , Ковлен Д.В. 2 , Кондратьева Е

Negligible effect of brain MRI data preprocessing for tumor segmentation

Ekaterina Kondrateva, Polina Druzhinina, Alexandra Dalechina, S V Zolotova et autres

ru, ca (code pays fourni par la source)

Negligible Effect of Brain MRI Data Preprocessing for Tumor Segmentation

Ekaterina Kondrateva, П. С. Дружинина, Alexandra Dalechina, S V Zolotova et autres

ru (code pays fourni par la source)

Negligible Effect of Brain MRI Data Preprocessing for Tumor Segmentation

Ekaterina Kondrateva, П. С. Дружинина, Alexandra Dalechina, S V Zolotova et autres

ru (code pays fourni par la source)

Metabolomic Profiling of the Blood of Patients with Chronic Consciousness Disorders

Anastasia Orlova, Ekaterina Kondrateva, Yaroslav A. Dubrovskii, N. V. Dryagina et autres

The main variants of chronic consciousness disorder (CCD) developing in adverse coma outcome are vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimal consciousness state (MCS). The aim of the study was to investigate the main differences in metabolomic abnormalities in patients with VS …

ru (code pays fourni par la source)

Negligible effect of brain MRI data preprocessing for tumor segmentation

Ekaterina Kondrateva, П. С. Дружинина, Alexandra Dalechina, Boris Shirokikh et autres

Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such as anatomy alignment, voxel resampling, signal intensity equalization, image denoising, …

BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance

П. С. Дружинина, Ekaterina Kondrateva, Arseny Bozhenko, V E Yarkin et autres

ru (code pays fourni par la source)

Fader networks for domain adaptation on fMRI: ABIDE-II study

Marina Pominova, Ekaterina Kondrateva, Maxim Sharaev, Alexander Bernstein et autres

ABIDE is the largest open-source autism spectrum disorder database with both fMRI data and full phenotype description. These data were extensively studied based on functional connectivity analysis as well as with deep learning on raw data, with top models accuracy close to …

ru (code pays fourni par la source)

Domain shift in computer vision models for MRI data analysis: an overview

Ekaterina Kondrateva, Marina Pominova, Elena Popova, Maxim Sharaev et autres

Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use and one of the reasons for that is poor transferability of the models to data from different sources …

ru (code pays fourni par la source)

Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia

Ruslan Aliev, Ekaterina Kondrateva, Maxim Sharaev, Олег Бронов et autres

ru (code pays fourni par la source)