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

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.

Segmentation Self-Supervised Learning +2

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

no code implementations • 29 Oct 2022 • Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci

Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.

Tumor Segmentation

An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

no code implementations • 15 Aug 2022 • Abhishek Srivastava, Debesh Jha, Elif Keles, Bulent Aydogan, Mohamed Abazeed, Ulas Bagci

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.

Image Segmentation Medical Image Segmentation +3