4 papers
Revealing Interconnections between Diseases: from Statistical Methods to Large Language Models
Alina Ermilova, Dmitrii Kornilov, Sofia Samoilova +5
Identifying disease interconnections through manual analysis of large-scale clinical data is labor-intensive, subjective, and prone to expert disagreement. While machine learning (…
Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation
Nadezhda Alsahanova, Pavel Bartenev, Maksim Sharaev +3
Background: Accurate lesion segmentation is critical for multiple sclerosis (MS) diagnosis, yet current deep learning approaches face robustness challenges. Aim: This study improve…
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
Sergey Pavlov, Alexey Artemov, Maksim Sharaev +2
Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to…
3D Deformable Convolutions for MRI classification
Marina Pominova, Ekaterina Kondrateva, Maksim Sharaev +3
Deep learning convolutional neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolutional deep neura…