no code implementations • 4 May 2023 • Ilkin Isler, Debesh Jha, Curtis Lisle, Justin Rineer, Patrick Kelly, Bulent Aydogan, Mohamed Abazeed, Damla Turgut, Ulas Bagci
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 learning.
1 code implementation • 3 Feb 2022 • Ilkin Isler, Curtis Lisle, Justin Rineer, Patrick Kelly, Damla Turgut, Jacob Ricci, Ulas Bagci
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients.