29 citations · 37 across the 10 of their papers we have counts for
5 papers · 1 filter
Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer
Jue Jiang, Chloe Min Seo Choi, Maria Thor +2
Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that…
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled example…
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets
Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang +1
Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution…
Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer
Josiah Simeth, Jue Jiang, Anton Nosov +4
Dose escalation radiotherapy allows increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL), motivating the development of automated me…
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation
Jue Jiang, Andreas Rimner, Joseph O. Deasy +1
Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatme…