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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May ABC Seminar: Cong Ma, PhD – Univ of Michigan “Modeling and understanding the spatial tumor evolution”
A tumor is a heterogeneous mixture of cancerous cells and multiple types of normal cells. Decoding this heterogeneity and identifying the genomic events that drive cancer development are key to […]
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April ABC Seminar: Maria Carilli – Caltech – “Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data”
Transcription in individual cells is an inherently stochastic process, with non-deterministic numbers of RNA molecules produced per gene in transcriptional events across cells of the same cell type and condition. […]
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February ABC Seminar: Prateek Prasanna, PhD – Stony Brook University – “Reality-aware Medical Vision: The Quest for Interpretable and Explainable AI in Medical Imaging”
Medical imaging plays a critical role in modern healthcare, yet its complexity necessitates advanced computational tools to enhance analysis, interpretability, and diagnostic accuracy. In this presentation, we will discuss our […]
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January ABC Seminar: Stephen Pfohl, PhD – Google Research – “Algorithmic fairness and responsible AI for health equity”
In this talk, I aim to present insights into the design of evaluations of machine learning and AI systems to assess properties related to algorithmic fairness and health equity. I […]
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November ABC Seminar: Brian Hie, PhD – Stanford University – “Sequence modeling from molecular to genome scale with Evo”
The genome is a sequence that encodes the DNA, RNA, and proteins orchestrating an organism’s function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on […]