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/