Nadeem Lab

Integrative Biomedical
Data Analysis

Nadeem Lab aims to analyze, interpret, and infer 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.

"Through the activities and contributions of the Nadeem lab, MSK is a MONAI contributor institution.  While MSK acknowledges MONAI as a useful resource for its community and may contribute content from time to time, MSK does not endorse any content, contribution or interpretation from this site, nor shall MSK’s contributions be interpreted or construed as MSK’s endorsement of any of the same. Any opinions, advice, statements, or other information or content submitted through MONAI, but not directly by MSK, are those of their respective authors and such authors are solely responsible for such submissions. MSK does not control, administer, monitor, review or fund any contributions to MONAI or its community. MSK takes no responsibility and assumes no liability for any content that any other user, contributor or third-party posts or sends over MONAI. Advice, statements, information or content provided by MSK via MONAI do not constitute, and shall not be construed as, medical advice or treatment recommendations. Under no circumstances will MSK be responsible for any loss or damage resulting from anyone’s reliance on information or other content posted on MONAI or transmitted to its users. Neither MSK’s contribution to MONAI nor any representation, variation or combination of MSK’s name, image, or logo (“MSK Marks”) confers any ownership, title, license or rights whether by implication, estoppel or otherwise, in whole or in part, in any MSK Marks and no consent is given for any use of MSK Marks whatsoever (including under any open source licenses governing contributions to MONAI) without MSK’s explicit and prior written approval."

RESEARCH

PUBLICATIONS

Updates

Apr 2024
Timeline Story

Dr. Nadeem will be giving an invited plenary talk at the 20th Annual Tucson Symposium - Roche Diagnostics USA.

Apr 2024
Timeline Story

Dr. Nadeem will be giving an invited talk at the Artificial Intelligence, Systems and Spatial Biology in Human Disease Conference at Mayo Clinic.

Apr 2024
Timeline Story

Dr. Nadeem will be giving an invited talk at the Artificial Intelligence, Systems and Spatial Biology in Human Disease Conference at Mayo Clinic.

Apr 2024
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Dr. Nadeem will be course faculty for the Annual 2024 Digital Pathology Course at Memorial Sloan Kettering Cancer Center.

Feb 2024
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Dr. Nadeem is serving as the Area Chair for MICCAI 2024.

Jan 2024
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Dr. Nadeem is teaching Machine Learning with Images course at Weill Cornell Graduate School of Medical Sciences.

Oct 2023
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Dr. Nadeem served as the Area Chair and Computational Pathology Oral Session Chair for MICCAI 2023.

Sep 2023
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Our paper on multi-institution Ki67 DeepLIIF validation in medullary thyroid carcinoma is now published in Histopathology'23.

May 2023
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Our paper on a new AI-ready restained and co-registered multiplex IHC and multiplex immunofluorescence dataset for reproducible and accurate characterization of tumor microenvironment is accepted for publication in MICCAI'23. The dataset is now published on The Cancer Imaging Archive.

Mar 2023
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Dr. Nadeem will serve on the  upcoming NIH Academic-Industrial Partnerships for Translation of Medical Technologies (AIP) review panel [Meeting Roster].
Jan 2023
Timeline Story
RMSim controlled respiratory motion simulation on static patient scans paper accepted to the top Medical Physics journal, PMB'23. Implementation with code and pre-trained models available on our SeqX2Y GitHub.
Jan 2023
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Dr. Nadeem will serve on the MONAI Advisory Board.

Jan 2023
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Dr. Nadeem will serve as an Area Chair for MICCAI 2023.

Jan 2023

Invited in-person talk at Bristol-Myers Squibb (Princeton).

Jan 2023
Dr. Nadeem is teaching Machine Learning with Images course at Weill Cornell Graduate School.
Nov 2022

Invited talk Stanford Medical Physics Grand Rounds on "Building a comprehensive multimodal/multiscale patient snapshot for improved clinical outcomes". Video recording available here.

Aug 2022
Timeline Story

Dr. Nadeem will serve on the MSK AI Ethics Leadership Committee.

Aug 2022
Timeline Story
Domain-knowledge drive 3D radiation dose prediction using moment-based loss function paper accepted to the top Medical Physics journal, PMB'22. Implementation with code, pre-trained models and docker container available on our DoseRTX GitHub.
Jun 2022
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Dr. Nadeem is the winner of the Annual MSK Departmental Research Excellence Award. Thank you to all the lab members, collaborators, and the administrative staff who supported our research.

May 2022
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Second MICCAI'22 paper is on color, lighting, texture, and specular reflection augmentation for colonoscopy videos available here. Implementation with pretrained models and docker container available on our Computational Endoscopy Platform GitHub.
May 2022
Timeline Story
Two papers accepted at MICCAI'22 (#1 Medical Imaging Conference). The first paper is on end-to-end clinically-interpretable radiomics and malignancy prediction available here. We release a large-scale dataset (containing annotations for clinically-reported features) along with an end-to-end deep learning pipeline for extracting clinically-interpretable radiomic features and malignancy prediction. Dataset, code, pretrained models and docker containers for reproducibility available on our GitHub here.
Mar 2022
Timeline Story
Acccompanying DeepLIIF cloud-native platform CVPR'22 paper available here. DeepLIIF can be run locally (GPU required) by pip installing the package and using the deepliif CLI command. DeepLIIF can be used remotely (no GPU required) through the https://deepliif.org website, the ImageJ plgin, or via the Cloud-API (details here).
Jan 2022
Timeline Story
DeepLIIF IHC quantification paper published in Nature Machine Intelligence. DeepLIIF cloud-native platform with user-friendly web interface is now available here for users to upload input images, visualize, and download results. Multi-GPU training and highly-optimized inference implementations are now available via our GitHub.
Oct 2021
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GitHub for a new higher-order exact statistical significance test, Coincidence Test, is now public. Explore the limitless applications across radiology, pathology and molecular data. Preprint is available here.
Oct 2021
Timeline Story
ImPartial interactive deep learning whole-cell segmentation GitHub library is now public. Preprint available here. Integration with MONAI Label, ImageJ, QuPath, and napari coming soon!
Oct 2021
Invited talk Warwick Tissue Image Analytics Seminar Series on "Mathematical Oncology Initiative". Video recording available here.
Aug 2021
Timeline Story
Spatial Profiling Toolbox (pre-release) GitHub is now public. Preprints and additional updates coming soon!
Aug 2021
Timeline Story
Physics-ArX library is now public on our GitHub. It provides (smart) physics-based data augmentation tools for robust quantification of CT and CBCT images during radiotherapy, as shown in  our PMB'21 (generalizable cross-modality esophagus segmentation) and Medical Physics'21 (multitask CBCT-to-CT translation and OAR segmentation) papers.
Aug 2021
Jo

Joseph Marino
(senior data scientist)
joins the lab.

Jun 2021
Timeline Story
Computational Endoscopy Platform (CEP) GitHub is now public with implementations of our MICCAI'21 (haustral fold detection), ISBI'21 (missing surface visualization), and CVPR'20 (scale-consistent depth inference) papers.
May 2021
Timeline Story
DeepLIIF IHC quantification preprint and code released.
Apr 2021

James Mathews (senior data scientist), Rami Vanguri (senior data scientist), Gunjan Shrivastava (data scientist), and Eliram Nof (postdoc) join the lab.

Jan 2021

Awarded $4.2M MSKCC DigITs funding for research and development of "institutional multiplex image analysis pipelines for biomarker quantification".

Jan 2021

Invited MSK Hospital Research Forum talk on "Spatial Profiling for Quantitative and Predictive Biomarkers".

Our Team

Saad Nadeem

(Principal Investigator)

James Mathews

(Senior Research Scientist)

Stephen Petrides

(Senior Research Scientist)

Joseph Marino

(Senior Research Scientist)

Gunjan Shrivastava

(Senior Research Scientist)

Carlin Liao

(Postdoc)