Lab profile
About Mahmood Lab
The lab develops machine learning, data fusion, and medical image analysis methods for objective diagnosis, prognosis, and biomarker discovery in computational pathology. They focus on developing automated mechanisms to reduce interobserver variability in cancer diagnosis using AI as an assistive tool for pathologists, and on identifying clinically relevant morphologic phenotypes and biomarkers associated with therapeutic response. The lab develops multimodal fusion algorithms combining information from multiple imaging modalities, patient histories, and multi-omics data for precise diagnostic, prognostic and therapeutic determinations.
01 · Research focus
Research areas and highlights
Topics and recent work as stated on the lab’s own site.
- UNI - Towards a general-purpose foundation model for computational pathologyA general purpose foundation model for computational pathology trained on 100 million pathology images from over 100k WSIs and adapted to over 30 different downstream tasks.
- PathChat - A Multimodal generative AI chatbot for pathologyA vision-language AI assistant for Pathology that can analyze histology images and answer diverse pathology-related queries.
- CONCH - Vision-language foundation model for pathologyA vision-language foundation model for computational pathology trained on 1.17 million pathology image-text pairs and adapted to 14 different downstream tasks.
- TriPath - Weakly Supervised AI for Efficient Analysis of 3D Pathology SamplesA method for weakly supervised AI for 3D pathology that enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes.
- TITAN - A multimodal whole slide foundation model for computational pathologyMultimodal whole slide foundation models published in Nature Medicine.
02 · People
Leadership and researchers
People listed on the lab’s team pages, by role.
03 · Research intelligence
Research Intelligence evidence
Publications, grants and clinical trials linked to the lab’s researchers in Health AI Central’s research data.
Publications & preprints
- Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.Nature biomedical engineering · 2026
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026
- The ADAPT learning cancer treatment system: ARPA-H's initiative to revolutionize cancer therapy.Cancer cell · 2026
Active NIH grants
No active NIH grants; 1 earlier grant linked to this lab’s researchers.
All 1 →04 · Updates
Updates and lab news
News and events from the lab’s own site.
No news has been collected from the lab’s site yet.
05 · Official presence
Lab sources and channels
The lab’s own pages and accounts.
06 · Related
Related labs and centers
Labs at the same institution and labs working on similar topics.