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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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2 papers · 1 filter

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…

cs.LG2025★ 1 cited

Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting

Mahad Ali, Curtis Lisle, Patrick W. Moore +3

Federated Learning (FL) provides a decentralized machine learning approach, where multiple devices or servers collaboratively train a model without sharing their raw data, thus ena…