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20182026
most citedROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset

55 citations · 60 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns

Darya Ardan, Valentin Oreiller, Henning Müller

Spatial mapping of lung adenocarcinoma (LUAD) growth patterns across whole slide images (WSIs) requires resolving architectural context at the region level, yet existing methods op…

cs.CV2025

Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

Yuan Jin, Antonio Pepe, Gian Marco Melito +36

The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack o…

cs.CV2024

Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge

Marek Wodzinski, Henning Müller

The automatic registration of noninvasive second-harmonic generation microscopy to hematoxylin and eosin slides is a highly desired, yet still unsolved problem. The task is challen…

cs.CV2024

Lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides

Zhuxian Guo, Amine Marzouki, Jean-François Emile +3

Lymphoid infiltration at tumor margins is a key prognostic marker in solid tumors, playing a crucial role in guiding immunotherapy decisions. Current assessment methods, heavily re…

cs.CV2024

Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation

Artur Jurgas, Marek Wodzinski, Marina D'Amato +3

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Ove…

cs.CV2023

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

Marek Wodzinski, Henning Müller

Automatic aorta segmentation from 3-D medical volumes is an important yet difficult task. Several factors make the problem challenging, e.g. the possibility of aortic dissection or…