3 citations · 4 across the 7 of their papers we have counts for
7 papers
Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcity
Ekaterina Sedykh, Salme Ussanov, Dmytro Fedorenko +1
Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive cases from the target site.…
Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering
Joonas Ariva, Dmytro Fishman
Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist effort to produce. Training on…
QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
Yaroslav Prytula, Anton Popov, Dmytro Fishman
Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structures that produce weak boundaries and mixed visual evidence in overlap re…
CellNet -- Localizing Cells using Sparse and Noisy Point Annotations
Benjamin Eckhardt, Dmytro Fishman, Stuart Fawke +3
Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes in humans via large scale sat…
IAUNet: Instance-Aware U-Net
Yaroslav Prytula, Illia Tsiporenko, Ali Zeynalli +1
Instance segmentation is critical in biomedical imaging to accurately distinguish individual objects like cells, which often overlap and vary in size. Recent query-based methods, w…
Going Beyond U-Net: Assessing Vision Transformers for Semantic Segmentation in Microscopy Image Analysis
Illia Tsiporenko, Pavel Chizhov, Dmytro Fishman
Segmentation is a crucial step in microscopy image analysis. Numerous approaches have been developed over the past years, ranging from classical segmentation algorithms to advanced…
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images
Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich +2
Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (…