1 citations · 1 across the 2 of their papers we have counts for
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
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Abhishek Srivastava, Debesh Jha, Elif Keles +3
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonst…