View a full listing of Harini Veeraraghavan: Publications’ journal articles.
Aneesh Rangnekar and Harini Veeraraghavan, “Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation”, in Transactions Machine Learning Research. 2026: 2026:7268. https://pubmed.ncbi.nlm.nih.gov/42370064/
Sudarshan Madhavan, Chengcheng Gui, Lando Bosma, Josiah Simeth, Jue Jiang, Nicolas Cote, Nima Hassan Rezaeian, Himanshu Nagar, Victoria Brennan, Neelam Tyagi, Harini Veeraraghavan. “Segmentation regularized training for multi-domain deep learning registration applied to magnetic resonance-guided prostate cancer radiotherapy”, Physics Imaging Radiation Oncology. 2026. 39:100989. DOI: 10.1016/j.phro.2026.100989. https://pubmed.ncbi.nlm.nih.gov/42232230/
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan. “Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images”, Proceedings of the 8th International Conference on Medical Imaging in Deep Learning, Proc Machine Learning Research 301: 679-694, 2026. https://pubmed.ncbi.nlm.nih.gov/42180651/
Chloe Min Seo Choi, Jue Jiang, Nikhil P Mankuzhy, Nishant Nadkarni, Sudarshan Madhavan, Abraham J Wu, Joseph O Deasy, Maria Thor, Andreas Rimner, Harini Veeraraghavan. “Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients”, Physics Imaging Radiation Oncology. 2026. 37:100907. DOI: 10.1016/j.phro.2026.100907. https://pubmed.ncbi.nlm.nih.gov/41631006/
Jorge Tapias Gomez, Nishant Nadkarni, Lando S Bosma, Jue Jiang, Ergys D Subashi, William P Segars, James M Balter, Mert R Sabuncu, Neelam Tyagi, Harini Veeraraghavan. “Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion”, Physics Medicine Biology. 71(1):015029. DOI: 10.1088/1361-6560/ae2b46. https://pubmed.ncbi.nlm.nih.gov/41370930/
Jiang J, Hong J, Tringale K, Reyngold M, Crane C, Tyagi N, Veeraraghavan H, “Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT”, Medical Physics. 2023. https://pubmed.ncbi.nlm.nih.gov/37265185/
Simeth J, Jiang J, Nosov A, Wibmer A, Zelefsky M, Tyagi N, Veeraraghavan H, “Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer”, Medical Physics. 2023. https://pubmed.ncbi.nlm.nih.gov/36856092/
Jiang J, Tyagi N, Tringale K, Crane C, Veeraraghavan H, “Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)”, Medical Image Computing and Computer Assisted Interventions 2022. https://pubmed.ncbi.nlm.nih.gov/36468915/
Jiang J, Elguindi S, Berry SL, Onochie I, Cervino L, Deasy JO, Veeraraghavan H. “Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT”, Medical Physics, 2022. https://pubmed.ncbi.nlm.nih.gov/35598077/ (This model is used for auto segmentation of head and neck organs in the MSK radiotherapy clinic).
Thompson HM, Kim JK, Jimenez-Rodriguez RM, Garcia-Aguilar J, Veeraraghavan H. “Deep-learning based model for identifying tumor in endoscopic images from patients with locally advanced rectal cancer treated with total neoadjuvant chemotherapy”, Diseases of Colon and Rectum, 2022. https://pubmed.ncbi.nlm.nih.gov/35358109/
Jiang J and Veeraraghavan H., “One-shot PACS: Patient specific anatomic context and shape prior aware recurrent registration-segmentation of longitudinal thoracic cone-beam CTs. IEEE Transactions on Medical Imaging, 2022. https://pubmed.ncbi.nlm.nih.gov/35213307/
Jiang J, Rimner A, Deasy J.O, Veeraraghavan H, “Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation”, IEEE Transactions on Medical Imaging, 2022. https://pubmed.ncbi.nlm.nih.gov/34855590/