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Neelam Tyagi
Researcher at Memorial Sloan Kettering Cancer Center
Publications - 107
Citations - 1783
Neelam Tyagi is an academic researcher from Memorial Sloan Kettering Cancer Center. The author has contributed to research in topics: Medicine & Radiation therapy. The author has an hindex of 20, co-authored 90 publications receiving 1163 citations. Previous affiliations of Neelam Tyagi include University of Michigan & University of California, San Diego.
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Book ChapterDOI
Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.
Jue Jiang,Yu-Chi Hu,Neelam Tyagi,Pengpeng Zhang,Andreas Rimner,Gig S. Mageras,Joseph O. Deasy,Harini Veeraraghavan +7 more
TL;DR: T tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets and semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs.
Journal ArticleDOI
Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis.
Neelam Tyagi,Sandra Fontenla,J Zhang,Michelle Cloutier,Mo Kadbi,J.G. Mechalakos,Michael J. Zelefsky,Joseph O. Deasy,Margie Hunt +8 more
TL;DR: MRI derived synthetic CT can be successfully used for a MR-only planning and treatment for prostate radiotherapy and has met institutional clinical objectives for target and normal structures.
Journal ArticleDOI
Clinical workflow for MR-only simulation and planning in prostate.
Neelam Tyagi,Sandra Fontenla,Michael J. Zelefsky,Marcia Chong-Ton,Kyle Ostergren,Niral Shah,Lizette Warner,Mo Kadbi,J.G. Mechalakos,Margie Hunt +9 more
TL;DR: MR-only simulation and planning with equivalent or superior target delineation, planning and treatment setup localization accuracy is feasible in a clinical setting because the temporal variations in normal structure was minimal.
Journal ArticleDOI
Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy.
Sharif Elguindi,Michael J. Zelefsky,Jue Jiang,Harini Veeraraghavan,Joseph O. Deasy,Margie Hunt,Neelam Tyagi +6 more
TL;DR: Transfer learning allows deep learning networks to retrain easily on small datasets and is successfully applied to auto-segment targets and OARs in prostate radiation therapy.
Journal ArticleDOI
Photon beam relative dose validation of the DPM Monte Carlo code in lung-equivalent media.
Indrin J. Chetty,Paule M. Charland,Neelam Tyagi,Daniel L. McShan,Benedick A. Fraass,Alex F. Bielajew +5 more
TL;DR: This work demonstrates that the DPM Monte Carlo code is capable of accurate photon beam dose calculations in situations where lateral electron disequilibrium effects are pronounced.