Nadeem Lab

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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.

Affiliated with Memorial Sloan Kettering Cancer Center

RESEARCH

PUBLICATIONS

Updates

Aug 2022
Timeline Story

Dr. Nadeem is the Chair of the MSK AI Ethics 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
Timeline Story

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
Timeline Story
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
Timeline Story
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 Data Scientist)

Rami Vanguri

(Senior Data Scientist)

Joseph Marino

(Senior Data Scientist)

Gunjan Shrivastava

(Data Scientist)

Eliram Nof

(Postdoc)