Department of Medical Physics

The Saad Nadeem Lab

Research

Saad Nadeem
Saad Nadeem, PhD
Associate Attending Computer Scientist

The Nadeem Lab analyzes, interprets, and infers novel insights from biomedical data at multiple scales (macro: radiology/ surgery, meso: pathology, micro: genomics/ transcriptomics/ proteomics/ metabolomics) for improving patient outcomes. We use advanced mathematical and machine learning techniques to drive this analysis. The lab focuses on building user-friendly tools that seamlessly fit into the clinical workflow and facilitate accurate and timely diagnosis/ prognosis/ decision making while aiding in novel biomarker discovery. We are the first academic lab in the world to have deployed two AI solutions in clinic across pathology (https://deepliif.org) and surgery (https://opwise.org).

Learn more at NadeemLab.org.

Projects

DeepLIIF: Deep Learning Inferred ImmunoFluorescence for virtual staining and cellular phenotyping on standard/clinical histology whole slide images (H&E and IHC). This is the first fully opensource (data/code/models) virtual staining computational pathology algorithm with commercial PathPresenter/EPIC integration deployed in clinic at MSK for accurate and reproducible IHC quantification (with anticipated processing workload of > 100,000 whole slide images per year). DeepLIIF has more than 20,000 downloads and our public cloud platform supports more than > 3,500 user sessions every day.

Spatial Multiomics Profiler for user-friendly, interactive, and reproducible spatial biomarker derivation and quantification for spatial multiomics datasets (histology, spatial proteomics, spatial transcriptomics, etc). We have already curated and released more than 12 spatial proteomics datasets on our SMProfiler platform (more coming with additional support for other single-cell modalities such as spatial transcriptomics and same-section histology). SMProfiler supports realtime spatial metrics computation from advanced mathematical and AI algorithms. It also has a built in slide viewer and support for realtime statistical significance testing and sharing of highlights or salient results with the community and other users.

Opwise for improving time and cost efficiency in robotic and laparascopy surgical procedures with realtime analysis and live feedback to the operating room staff. This is deployed at MSK currently for Robotic Assisted Radical Cystectomy with support for other procedures coming soon.

View Lab Overview

Featured News

Saad Nadeem, PhD, Assistant Attending Computer Scientist
MSK Partners with PathPresenter to Integrate New AI Module for Oncology Research
Memorial Sloan Kettering Cancer Center (MSK) is partnering with PathPresenter, a leader in digital pathology workflow software, to integrate DeepLIIF, MSK’s new artificial intelligence (AI) algorithm, into PathPresenter’s image management platform for oncology research.

Publications Highlights

Zehra, Talat, Joseph Marino, Wendy Wang, Grigoriy Frantsuzov, and Saad Nadeem. “Rethinking histology slide digitization workflows for low-resource settings.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 427-436. Cham: Springer Nature Switzerland, 2024.

Nadeem, Saad, Matthew G. Hanna, Kartik Viswanathan, Joseph Marino, Mahsa Ahadi, Bayan Alzumaili, Mohamed‐Amine Bani et al. “Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms.” Histopathology 83, no. 6 (2023): 981-988.

Ghahremani, Parmida, Joseph Marino, Juan Hernandez-Prera, Janis V. de la Iglesia, Robbert JC Slebos, Christine H. Chung, and Saad Nadeem. “An AI-ready multiplex staining dataset for reproducible and accurate characterization of tumor immune microenvironment.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 704-713. Cham: Springer Nature Switzerland, 2023.

Ghahremani, Parmida, Yanyun Li, Arie Kaufman, Rami Vanguri, Noah Greenwald, Michael Angelo, Travis J. Hollmann, and Saad Nadeem. “Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification.” Nature machine intelligence 4, no. 4 (2022): 401-412.

Ghahremani, Parmida, Joseph Marino, Ricardo Dodds, and Saad Nadeem. “Deepliif: An online platform for quantification of clinical pathology slides.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21399-21405. 2022.

View All Publications

People

Saad Nadeem

Saad Nadeem, PhD

Associate Attending Computer Scientist

  • We develop and clinically deploy novel computational algorithms for analyzing multiscale, multimodal biomedical imaging data.
  • Education: PhD, Stony Brook University

Members

Carlin Liao

Postdoctoral Fellow

Joseph Marino

Senior Research Scientist

James Mathews

Senior Research Scientist

Stephen Petrides

Senior Research Scientist

Gunjan Shrivastava

Senior Research Scientist

Josef Zhu

Graduate Student

Navdeep Dahiya

Visiting PhD Student (Georgia Tech)

Rena Elkin

PhD Student

Parmida Ghahremani

PhD Student

Natalia Martinez Gil

Visiting PhD Student (Duke)

Shreeraj Jadhav

PhD Student

Donghoon Lee

Postdoctoral Fellow

Shawn Mathew

PhD Student

Sadegh Riyahi

Postdoctoral Fellow

Alexandre Tiard

Visiting PhD Student (UCLA)

Rami Vanguri

Postdoctoral Fellow

Achievements

  • Faculty Research Excellence Award, Medical Physics (2025)
  • DeepLIIF deployed in clinic with anticipated workload of 100,000 WSIs per year (2025)
  • Opwise surgery AI solution deployed in clinic to improve cost/time efficiency in OR (2025)
  • MONAI advisory board and Chair for Human-AI collaboration working group (2023)
  • Faculty Research Excellence Award, Medical Physics (2022)

Open Positions

To learn more about available postdoctoral opportunities, please visit our Career Center

To learn more about compensation and benefits for postdoctoral researchers at MSK, please visit Resources for Postdocs