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

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

eess.IV2023★ 1 cited

Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification

Ilkin Isler, Debesh Jha, Curtis Lisle +6

In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised…

eess.IV2022

Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks

Ilkin Isler, Curtis Lisle, Justin Rineer +4

Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segme…