Dr. Mahmood is an Assistant Professor of Pathology at Harvard Medical School and the Division of Computational Pathology at the Brigham and Women's Hospital. He received his Ph.D. in Biomedical Imaging from the Okinawa Institute of Science and Technology, Japan and was a postdoctoral fellow at the department of biomedical engineering at Johns Hopkins University. His research interests include pathology image analysis, morphological feature, and biomarker discovery using data fusion and multimodal analysis. Dr. Mahmood is a full member of the Dana-Farber Cancer Institute / Harvard Cancer Center ; an Associate Member of the Broad Institute of Harvard and MIT, and a member of the Harvard Bioinformatics and Integrative Genomics (BIG) faculty.
Recent Key Publications
- R. J. Chen, M. Y. Lu, D. K. Williamson, T. Y. Chen, ...., B. Joo, and F. Mahmood*, "Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning" Cancer Cell (2022).
- M. Y. Lu, T. Y. Chen, D. K. Williamson, M. Zhao, M. Shady, J. Lipkova & F. Mahmood*, AI-based pathology predicts origins for cancers of unknown primary. Nature (2021).
- J. Lipkova, T. Y. Chen, M. Y. Lu, R. J. Chen, M. Williams,..., K. E. Odening, and F. Mahmood*. Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies. Nature Medicine (2022).
- M. Y. Lu, D. K. Williamson, T. Y. Chen, R. J. Chen, M. Barbieri & F. Mahmood* Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering (2021).
- R. J. Chen, M. Y. Lu, J. Wang, D. K. Williamson, S. J. Rodig, N. I. Lindeman, & F. Mahmood* Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Transactions on Medical Imaging (2021).
See more at: www.mahmoodlab.org
Mahmood Lab (Pathology Image Analysis Laboratory) aims to utilize machine learning, data fusion, and medical image analysis to develop streamlined workflows for cancer diagnosis, prognosis, and biomarker discovery. We are interested in developing automated and objective mechanisms for reducing interobserver and intraobserver variability in cancer diagnosis using artificial intelligence as an assistive tool for pathologists. The lab also focuses on the development of new algorithms and methods to identify clinically relevant morphologic phenotypes and biomarkers associated with response to specific therapeutic agents. We develop multimodal fusion algorithms for combining information from multiple imaging modalities, familial and patient histories and multi-omics data to make more precise diagnostic, prognostic and therapeutic determinations.