AI for Pathology Image Analysis
Mahmood Lab aims to utilize machine learning, data fusion, and medical image analysis to develop streamlined workflows for objective 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. We are affiliated with the Harvard Data Science Initiative; the Harvard Bioinformatics and Integrative Genomics (BIG) program; the Cancer Data Science Program at the Dana-Farber Cancer Institute and the Cancer Program at the Broad Institute of Harvard and MIT.
🏢8002-K, HBTM, 60 Fenwood Rd., Boston, MA 02115
Tarissa A. Mages tarnoldmages<at>bwh.harvard.edu 617-525-8953
Hear about our work – Invited Talks
Upcoming Invited Talks: 07/21/21 IEEE EMBS Seminar Series ; 07/23/21 AstraZeneca Pathology Grand Rounds ; 07/27/21 ISCB ISMB 2021 ; 08/05/21 DPA Webinar Series ; 08/29/21 European Pathology Congress ; 09/13/21 Northshore Pathology Grand Rounds ; 09/22/21 CMU-Pitt ML in Medicine Seminar Series ; 09/26/21 World CUP Awareness Week ; 09/27/21 MICCAI COMPAY Workshop ; 10/4/21 U of Houston EECS Seminar Series ; 10/13/21 Global Conference on Regulatory Science ; 10/22/21 Duke Pathology Grand Rounds ; 11/8/21 Case AI in Oncology Symposium; 11/10/21 The Economist 7th Annual World Cancer Series ; 11/15/21 Warwick TIA Seminar Series. 11/17/21 MGH Pathology Grand Rounds ; 2/17/22 ML4H Broad Institute ; 2/18/22 MD Anderson Pathology Grand Rounds ; 2/20/22 SPIE Medical Imaging (Digital Pathology Keynote) ; 4/2/22 ASIP Experimental Biology Conference ; 4/3/22 Roche Tucson Symposium 2022 ; 4/24/22 OSA Biophotonics Congress ; 5/9/22 Pathology Informatics Summit ; 5/24/22 SLAS Europe ; 8/25/22 ASCO Breakthrough Meeting
Recent Media Coverage
- HMS News: Heart Saving AI
- ASCO Post: AI System May Aid in Diagnosing Cancer of Unknown Primary
- HMS News: Predicting Cancer’s Epicenter
- Nvidia News: Under the Microscope: Top Pathology Lab Fuses Data Sources to Develop Cancer-Detecting AI
- Forbes: Transforming Federal Healthcare with AI
- Bioengineering Today: How to Train Your Radiology AI
- Auntminnie.com: Cinematic rendering bolsters AI for depth estimation on endoscopy
- JHU News: Johns Hopkins School of Medicine Excellence in Research Award
- June’22: The lab is now officially part of MGH Pathology, we are excited about the new opportunities and potential collaborations.
- June’22: Our work on Hierarchical Image Pyramid Transformer (HIPT) was accepted at CVPR 2022 (paper, code, oral talk). And our work on incorporating intratumoral heterogeneity into deep models for pathology via variance pooling is accepted at MICCAI 2022 (paper; more soon).
- May’22: Congratulations to Mane Williams for passing his qualifying exam.
- April’22: Congratulations to our postdocs Iain Carmichael and Tiffany Chen for accepting their next roles. Iain will be joining the faculty at UC Berkeley statistics this fall, and Tiffany Chen will join Bigene as the Director of Pathology.
- Mar’22: We are excited to welcome HST PhD student Anurag Vaidya and postdoctoral fellows Guillaume Jaume and Kutsev Bengisu.
- Mar’22: Our deep learning-enabled assessment of cardiac transplant rejection study is published in Nature Medicine, see: journal link; demo; code ; HMS News.
- Jan’22: HST PhD Student Daniel Shao; BIG PhD Student Mane Williams; Masters students Elea Bach, Tong Ding and research associate Lottie Zhuang joined the lab – welcome.
- Dec’21: Our Federated Learning for Computational Pathology study is published in Medical Image Analysis.
- Sep’21: We are excited to welcome postdoctoral fellows Iain Carmichael and Andrew Song to the lab.
- Aug’21: Honored to be named on The Pathologist power list 2021.
- July’21: Multimodal co-attention transformers (MCAT) accepted at ICCV 2021, (read-link; code) . Patch-GCN: Context-aware survival prediction accepted at MICCAI 2021 (read-link; code).
- July’21: New pre-prints: SISH for fast and scalable histology image search (pre-print; code); DL-based Frozen Section to FFPE translation (pre-print; code); PORPOISE: Pan-cancer integrative histology-genomic analysis via DL (pre-print; code).
- June’21: Congratulations to our PhD students Richard Chen (HMS/BIG) and Sharifa Sahai (HMS/SysBio) for passing their qualifying exams.
- June’21: Our comment on synthetic data for machine learning in medicine is published in Nature Biomedical Engineering and our spotlight on using multiplex cPath for treatment response prediction is published in Cancer Cell.
- May’21: Our AI-based cancer origin prediction using conventional histology study is published in Nature check out the demo, code and HMS news story.
- April’21: Congratulations to Sharifa Sahai for getting the NSF Fellowship and to Max Lu for getting the Tau Beta Pi Fellowship!
- March’21: CLAM is now in print at Nature Biomedical Engineering see our opensource code and demo.
- Dec’20: Mahmood lab will be presenting one plenary talk and three posters at the AACR AI conference.
- Nov’20: Rotation students Mane Williams and Trevor Manz joined the lab – welcome.
- Nov’20: Congratulations to Andrew and Bowen for winning awards at Pathology Visions 2020 ; Andrew and Max Lu received Discover Brigham Awards and Max Lu was also awarded the Brigham Research Excellence Award 2020.
- Sept’20: New pre-print: Federated Learning for Whole Slide Computational Pathology.
- Sept’20: The lab has been awarded a MIRA/R35 Outstanding Investigator Award by NIH/NIGMS for 2020-2025!
- Aug’20: Pathomic Fusion accepted to IEEE Transactions on Medical Imaging.
- Aug’20: Andrew Schaumberg and Muhammad Shaban joined the lab as postdocs – welcome.
- June’20: New demo: TOAD, New code: TOAD.
- June’20: New preprint: Tumor Origin Prediction via Deep Learning.
- April’20: New code release: CLAM.
- April’20: New preprint: CLAM, check our interactive demo here.
- April’20: Maha, Dehan, and John joined the lab – welcome.
- April’20: Upcoming invited talk at Virtual Pathology Grand Rounds (Hosted by UCSF).
- Feb’20: Upcoming invited talk at Nvidia GTC San Jose 2020.
- Feb’20: Jana, Sharifa, Matteo, Bowen, and MyeongSeo joined the lab – welcome.
- Feb’20: Mahmood lab presenting three oral talks at SPIE Medical Imaging.
- Feb’20: Upcoming nano-course on Deep Learning for Biomedical Image Analysis.
- Jan’20: Upcoming invited talk at the FDA on 01/15.
- Dec’19: New code release: Pathomic Fusion
- Dec’19: New pre-print: Pathomic Fusion (Deep Multimodal fusion for integrating histology and -omic data for diagnosis, prognosis).
- Dec’19: Article accepted IEEE Transactions on Medical Imaging.
- Nov’19: Mahmood Lab will be presenting four abstracts at USCAP 2020 in Los Angeles.
- Nov’19: Upcoming Invited Talk at CCDS Grand Rounds.
- Nov’19: Mahmood Lab featured in Nvidia News: Under the Microscope: Top Pathology Lab Fuses Data Sources to Develop Cancer-Detecting AI
- Nov’19: Mahmood lab will be presenting three posters at Discover Brigham 2019.
- Oct’19: New Pre-print GCNs for Prostate TMA classification (ISBI 2020 submission).
- Oct’19: New Pre-print MIL+CPC for semi-supervised histology classification (Accepted for presentation at NeurIPS ML4H 2019).
- Oct’19: Dr. Mahmood becomes an Associate Member of the Broad Institute of Harvard and MIT.
- Oct’19: Max Lu and Richard Chen awarded the NeurIPS 2019 ML4H travel award – congratulations.
- Oct’19: Mahmood Lab presenting three oral talks at SPIE Medical Imaging 2019 – Digital Pathology Conference in Houston.
- Oct’19: Two papers accepted at NeurIPS ML4H 2019. Pre-prints will be available soon. Visit us in Vancouver to learn more.
- Aug’19: Dr. Mahmood will be giving an invited talk at Nvidia GTC DC 2019.
- Aug’19: Mahmood Lab giving one oral and two poster presentations at Pathology Visions 2019 in Orlando.
- July’19: New pre-print: GANPOP: GAN-based Prediction of Optical Properties from Single Snapshot Wide-field Images.
- July’19: Dr. Mahmood becomes a full member of the cancer data sciences program at the Dana-Farber Cancer Institute / Harvard Cancer Center.
- July’19: Jingwen Wang, Research Intern joined the lab – welcome.
- July’19: Paper accepted at 2019 KDD Workshop on Applied Data Science for Healthcare.
- July’19: Dr. Mahmood becomes a member of the Harvard Bioinformatics and Integrative Genomics (BIG) faculty.
- June’19: Article accepted in IEEE Robotics and Automation Letters.
- June’19: Max Lu, Research Assistant joined the lab – welcome.
- June’19: Mahmood Lab welcomes its first member, Richard Chen, Harvard BIG PhD Student joined the lab.
- May’19: Article accepted in IEEE Transactions on Medical Imaging.
- April’19: Paper accepted in ICLR 2019 Workshop.
- April’19: Mahmood lab opens its doors, check out careers for postdoctoral fellowships.
- Mar’19: Article accepted in Biomedical Optics Express.
- Dec’18: New Pre-Print: Diffuser Wavefront Sensor
- Nov’18: New Pre-Print: Multimodal DenseNet
- Oct’18: New Pre-Print: Deep LSR
- Oct’18: New Pre-Print: Nuclei Segmentation in Histopathology Data.
- Oct’18: 3 oral and 2 poster presentations accepted at SPIE Medical Imaging.
- Oct’18: 2 oral and 2 poster presentations accepted at SPIE Photonics West.
- Oct’18: Invited talk at Nvidia GTC DC 2018.
- Sept’18: Invited talk at SAS Deep Learning Symposium.
- Aug’18: Article accepted in Physics in Medicine and Biology.
- July’18: Article accepted in Medical Image Analysis.
- March’18: Article accepted in IEEE Transactions on Medical Imaging.
- March’18: Poster presented at Nvidia GTC 2018.
- March’18: Poster presented at SPIE Medical Imaging 2018.
- Feb’18: Article accepted in IEEE International Symposium in Biomedical Imaging (ISBI).
- Jan’18: Oral talk presented at SPIE Photonics West 2018.
- Jan’18: Article accepted in IEEE Signal Processing Letters.
- July’18: Article accepted in IEEE Access.
- June 2017: Article accepted in International Journal of Reconfigurable Computing.