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

  • Associate Professor, Pathology, Harvard Medical School
  • Associate Professor, Division of Computational Pathology, Brigham And Women's Hospital
  • Associate Member, Cancer Program, Broad Institute of MIT and Harvard

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 from Harvard Catalyst Profiles

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  • Ozyoruk KB, Can S, Darbaz B, Başak K, Demir D, Gokceler GI, Serin G, Hacisalihoglu UP, Kurtuluş E, Lu MY, Chen TY, Williamson DFK, Yılmaz F, Mahmood F, Turan M. A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nat Biomed Eng 2022; 6:1407-1419. PubMed
  • Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, Shaban M, Shady M, Williams M, Joo B, Mahmood F. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 2022; 40:865-878.e6. PubMed
  • Ghaffari Laleh N, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-Claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med Image Anal 2022; 79:102474. PubMed
  • Lu MY, Chen RJ, Kong D, Lipkova J, Singh R, Williamson DFK, Chen TY, Mahmood F. Federated learning for computational pathology on gigapixel whole slide images. Med Image Anal 2022; 76:102298. PubMed
  • Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, Mahmood F. AI-based pathology predicts origins for cancers of unknown primary. Nature 2021. PubMed
  • Ozyoruk KB, Gokceler GI, Bobrow TL, Coskun G, Incetan K, Almalioglu Y, Mahmood F, Curto E, Perdigoto L, Oliveira M, Sahin H, Araujo H, Alexandrino H, Durr NJ, Gilbert HB, Turan M. EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Med Image Anal 2021; 71:102058. PubMed
  • Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 2021. PubMed
  • İncetan K, Celik IO, Obeid A, Gokceler GI, Ozyoruk KB, Almalioglu Y, Chen RJ, Mahmood F, Gilbert H, Durr NJ, Turan M. VR-Caps: A Virtual Environment for Capsule Endoscopy. Med Image Anal 2021; 70:101990. PubMed
  • Chen RJ, Lu MY, Wang J, Williamson DFK, Rodig SJ, Lindeman NI, Mahmood F. Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis. IEEE Trans Med Imaging 2020. PubMed
  • Almalioglu Y, Ozyoruk KB, Gokce A, Incetan K, Simsek GIGMA, Ararat K, Chen RJ, Durr NJ, Mahmood F, Turan M. EndoL2H: Deep Super-Resolution for Capsule Endoscopy. IEEE Trans Med Imaging 2020. PubMed
  • Gurney-Champion OJ, Mahmood F, van Schie M, Julian R, George B, Philippens MEP, van der Heide UA, Thorwarth D, Redalen KR. Quantitative imaging for radiotherapy purposes. Radiother Oncol 2020; 146:66-75. PubMed
  • Chen MT, Mahmood F, Sweer JA, Durr NJ. GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images. IEEE Trans Med Imaging 2019. PubMed
  • 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, Durr NJ. Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal 2018; 48:230-243. PubMed
  • Mahmood F, Chen R, Durr NJ. Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training. IEEE Trans Med Imaging 2018; 37:2572-2581. 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 2016; 11:e0148332. PubMed
  • Mahmood F, Shahid N, Vandergheynst P, Skoglund U. Graph-based sinogram denoising for tomographic reconstructions. Conf Proc IEEE Eng Med Biol Soc 2016; 2016:3961-3664. 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 2014; 2014:5438-41. PubMed
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