Faisal Mahmood

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Associate Professor (Lead Investigator)

Harvard Medical School

United States

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About

Faisal Mahmood is an Associate Professor and Lead Investigator at Harvard Medical School, United States. His research focuses on biomedical machine learning, artificial intelligence applications in cancer research, and computational pathology. He has contributed to multiple recent publications addressing topics such as algorithmic fairness in healthcare and innovative methodologies for histology image analysis.

Recent Grants

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INTERPRETABLE DEEP LEARNING ALGORITHMS FOR PATHOLOGY IMAGE ANALYSIS

Open Date: 2020-09-01

Close Date: 2025-08-01

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Collaborators (7)

Jana Lipková

Professor

University of California, Irvine

UNITED STATES

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Eva Curto

PORTUGAL

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Jakob Nikolas Kather

Professor of Clinical Artificial Intelligence

TU Dresden

GERMANY

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Hunter Gilbert

Assistant Professor

Louisiana State University

UNITED STATES

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Katja E Odening

SWITZERLAND

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Nicholas J. Durr

Johns Hopkins University

UNITED STATES

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Luis Perdigoto

Professor Adjunto

Instituto Politécnico de Leiria Escola Superior de Tecnologia e Gestão

PORTUGAL

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Faisal Mahmood

Research Interests

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About

Recent Grants

Articles (16)

A benchmarking crisis in biomedical machine learning

A guide to artificial intelligence for cancer researchers

cAMP/PKA signaling regulates TDP-43 aggregation and mislocalization

Are Stronger Feature Representations All You Need for Histology Image Search?

Harnessing medical twitter data for pathology AI

Algorithmic fairness in artificial intelligence for medicine and healthcare

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status

Artificial intelligence for digital and computational pathology

A unifying force for the realization of medical AI

Fast and scalable search of whole-slide images via self-supervised deep learning

Integrating context for superior cancer prognosis

Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

EndoSLAM Dataset and an Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos

Synthetic data in machine learning for medicine and healthcare

AI-based pathology predicts origins for cancers of unknown primary

Collaborators (7)

Jana Lipková

Eva Curto

Jakob Nikolas Kather

Hunter Gilbert

Katja E Odening

Nicholas J. Durr

Luis Perdigoto

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