Photo of Faisal Mahmood,  PhD

Faisal Mahmood, PhD

Brigham And Women's Hospital

Brigham And Women's Hospital
Phone: (857) 307-5225


faisalmahmood@bwh.harvard.edu

Faisal Mahmood, PhD

Brigham And Women's Hospital

EDUCATIONAL TITLES

  • Assistant Professor, Pathology, Harvard Medical School
  • Assistant Professor, Division of Computational Pathology, Brigham And Women's Hospital

DF/HCC PROGRAM AFFILIATION

Research Abstract

The Mahmood Lab at the Brigham and Women's Hospital 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 are also interested in fusing multimodal information from multiple imaging modalities, familial and patient histories and multi-omics data to make more precise diagnostic, prognostic and therapeutic determinations.

Publications

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  • Mahmood F, Borders D, Chen R, McKay GN, Salimian KJ, Baras A, Durr NJ. Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images. IEEE Trans Med Imaging 2019. PubMed
  • Bobrow TL, Mahmood F, Inserni M, Durr NJ. DeepLSR: a deep learning approach for laser speckle reduction. Biomed Opt Express 2019; 10:2869-2882. PubMed
  • McKay GN, Mahmood F, Durr NJ. Large dynamic range autorefraction with a low-cost diffuser wavefront sensor. Biomed Opt Express 2019; 10:1718-1735. PubMed
  • Mahmood F, Chen R, Sudarsky S, Yu D, Durr NJ. Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images. Phys Med Biol 2018; 63:185012. PubMed
  • Mahmood F, Chen R, Durr NJ. Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training. IEEE Trans Med Imaging 2019; 37:2572-2581. PubMed
  • Mahmood F, Durr NJ. Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal 2019; 48:230-243. PubMed
  • Mahmood F, Shahid N, Vandergheynst P, Skoglund U. Graph-based sinogram denoising for tomographic reconstructions. Conf Proc IEEE Eng Med Biol Soc 2018; 2016:3961-3664. PubMed
  • Mohan DM, Kumar P, Mahmood F, Wong KF, Agrawal A, Elgendi M, Shukla R, Ang N, Ching A, Dauwels J, Chan AH. Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach. PLoS ONE 2019; 11:e0148332. PubMed
  • Kumar P, Mahmood F, Mohan DM, Wong K, Agrawal A, Elgendi M, Shukla R, Dauwels J, Chan AH. On the effect of subliminal priming on subjective perception of images: a machine learning approach. Conf Proc IEEE Eng Med Biol Soc 2018; 2014:5438-41. PubMed