BWH COMPUTATIONAL PATHOLOGY
Our Mission
Improve human health
Our core mission is to alleviate human suffering by reducing the burden of diseases on individuals and on the population. This mission informs all our activities in developing and applying computational technologies, which we leverage to make an impact on a broad range of human diseases including infectious, cancer, heart, kidney, intestinal, autoimmune, allergies, and neurological disorders.
Advance the field of pathology
Pathology is both a scientific and a medical discipline, involving the study of basic mechanisms of diseases and the diagnosis of diseases using tissue and fluid samples. Our goal is to advance the field of pathology through computational technologies, to improve the understanding, diagnosis, and treatment of human diseases. With this broad view of pathology, we work on a variety of applications such as deep learning/artificial intelligence to improve cancer diagnosis/prognosis from histology images and to create new live bacterial therapeutics to treat infectious or autoimmune diseases.
Develop innovative computational methods
Human diseases often have complex causes and effects on the body. Data needed to analyze human diseases is similarly complex and multi-faceted. These data are also often difficult to acquire, leading to relatively limited dataset sizes. These and other challenges necessitate going beyond application of existing computational methods. Thus, we actively engage in computational research, to develop novel computational models, inference algorithms, integrated pipelines, and hardware. To accomplish this, we leverage a variety of advanced computational disciplines, including Bayesian nonparametric statistics, deep learning and control theory.
Division Opportunities
Division News
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Special Seminar: Anshul Kundaje, PhD – “Using deep learning models to debug regulatory genomics experiments and decode cis-regulatory syntax”
BWH Computational Pathology Special Seminar Title: Using deep learning models to debug regulatory genomics experiments and decode cis-regulatory syntax Speaker: Anshul Kundaje, PhD Affiliation: Stanford University Position: Associate Professor, Genetics […]
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Nature Medicine Publications detail Mahmood Lab’s Design of AI Foundation Models to Advance Pathology
Two foundation models for pathology AI developed by the Mahmood Lab published in Nature Medicine: UNI and CONCH. Foundation models, advanced artificial intelligence systems trained on large-scale datasets, hold the […]
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March ABC Seminar: Andrew H. Song – Brigham and Women’s Hospital – “Towards 3D pathology – The opportunities and challenges”
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross sections that can poorly represent the tissue due to sampling bias. To […]
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February ABC Seminar: Vitalii Kleshchevnikov, PhD – Wellcome Sanger Institute – “Probabilistic models to resolve cell identity and tissue architecture”
Cell identity drives cell-cell communication and tissue architecture and is in return regulated by cell-extrinsic cues. Cell identity is determined by the combination of intrinsic developmentally established transcription factor use […]