5 papers
Deep Learning for Automated Quantification of Tumor-Associated Macrophages from H&E-Stained Slides in Diffuse Large B-Cell Lymphoma
Anastasiia Studenikina, Svetlana Illarionova, Joaquim Carreras +7
While M2-polarized tumor-associated macrophages (TAMs) have been established as indicators of disease aggressiveness in diffuse large B-cell lymphoma (DLBCL), traditional CD163 imm…
HAPS: Rethinking Image Similarity for Virtual Staining
Fedor Gubanov, Svetlana Illarionova, Vlad Kozlovskiy +6
Virtual staining of histopathology images (e.g., H&E-IHC) is an emerging tool in digital pathology, enabling faster and cheaper workflows by synthesizing target stains from routine…
GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning
Evgeny Alves Limarenko, Anastasiia Studenikina, Svetlana Illarionova +1
In multi-task learning (MTL), gradient conflict poses a significant challenge. Effective methods for addressing this problem, including PCGrad, CAGrad, and GradNorm, in their origi…
impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction
Maria Boyko, Aleksandra Beliaeva, Dmitriy Kornilov +2
The use of diverse modalities, such as omics, medical images, and clinical data can not only improve the performance of prognostic models but also deepen an understanding of diseas…
Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Rinat Prochii, Elizaveta Dakhova, Pavel Birulin +1
Accurate classification of second-trimester fetal ultrasound images remains challenging due to low image quality, high intra-class variability, and significant class imbalance. In…