Saad Nadeem
Memorial Sloan Kettering Cancer Center
99 Papers
172 Citations
Saad Nadeem is an academic researcher from Memorial Sloan Kettering Cancer Center. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 10, co-authored 75 publications. Previous affiliations of Saad Nadeem include Stony Brook University.
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Papers
Optimal Mass Transport with Lagrangian Workflow Reveals Advective and Diffusion Driven Solute Transport in the Glymphatic System.
Sunil Koundal,Rena Elkin,Saad Nadeem,Yuechuan Xue,Stefan Constantinou,Simon Sanggaard,Xiaodan Liu,Brittany Monte,Feng Xu,William E. Van Nostrand,Hedok Lee,Joanna M. Wardlaw,Helene Benveniste,Allen Tannenbaum +13 more
TL;DR: A regularized version of the optimal mass transport (rOMT) problem, wherein the advection/diffusion equation is the only a priori assumption required, provides novel insights in the local dynamics of glymphatic system transport that may have implications for neurodegenerative diseases.
Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification
Parmida Ghahremani,Yanyun Li,Arie Kaufman,Rami Vanguri,Noah F. Greenwald,Michael Angelo,Travis J. Hollmann,Saad Nadeem +7 more
TL;DR: DeepLIIF as mentioned in this paper is a multi-task deep learning framework for cell segmentation and quantification on IHC and multiplex immunofluorescence (mpIF) images.
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Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification
Parmida Ghahremani,Yanyun Li,Arie Kaufman,Rami Vanguri,Noah F. Greenwald,Michael Angelo,Travis J. Hollmann,Saad Nadeem +7 more
TL;DR: DeepLIIF as mentioned in this paper is a multi-task deep learning framework for cell segmentation and quantification on IHC and multiplex immunofluorescence (mpIF) images.
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Robust and interpretable PAM50 reclassification exhibits survival advantage for myoepithelial and immune phenotypes
James C. Mathews,Saad Nadeem,Arnold J. Levine,Maryam Pouryahya,Joseph O. Deasy,Allen Tannenbaum +5 more
- 09 Sep 2019
TL;DR: The Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression, and exhibits the best overall survival, while tumors in the luminal class (concordant with Luminal A) may be more aggressive than previously thought.
Computer-Aided Detection of Polyps in Optical Colonoscopy Images.
Saad Nadeem,Arie E. Kaufman +1 more
TL;DR: An automatic detection algorithm is presented that uses a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then uses a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image.
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