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20222026
most citedSelf-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification

1 citations · 3 across the 5 of their papers we have counts for

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cs.CV2026

Sharing standardized image-derived data in computational pathology using DICOM

Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13

Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…

cs.CV2025

Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing

Ilkin Sevgi Isler, David Mohaisen, Curtis Lisle +2

Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertain…