29 citations · 37 across the 10 of their papers we have counts for
11 papers
Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
Jue Jiang, Harini Veeraraghavan
Methods: Nine SSL methods spanning four pretext-task families were pretrained from scratch using the same 10{,}412 3D CT scans (1.89~M 2D axial slices) covering varied disease site…
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random…
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard Nürnberg, Dennis Bontempi, Suraj Pai +17
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are…
Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy
Sudharsan Madhavan, Chengcheng Gui, Lando Bosma +8
Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained a…
Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma +7
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging…
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…
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis
Jue Jiang, Aneesh Rangnekar, Chloe Min Seo Choi +1
Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted w…
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…