Our laboratory focuses on research in the novel field of Computational Pathology. We develop and apply quantitative methods for the analysis of digital microscopy slides and relate the resulting statistical descriptors to patient outcomes.
Specifically, we use supervised and unsupervised machine-learning algorithms and methods from computer vision and biostatistics to extract information from terabytes of data generated by modern pathology departments to gain insight into research questions such as how genetic mutations influence tissue morphology or which combinations of immunohistochemical markers predict patient survival.
In addition to these research questions, we aim to improve clinical practice in pathology by developing intelligent decision-support systems that not only automate cumbersome or repetitive tasks but also lead to more-objective and reproducible results. The overarching goal is to lead the way in transforming pathology from a qualitative to a quantitative science.
The Team
- Thomas J. Fuchs, Dr. sc. – Principal Investigator
- Gabriele Campanella, PhD – Senior Computational Scientist
- Silke Muehlstedt, PhD – Director of Research and Strategic Partnerships
Previous Members
- Christina Virgo, Director of Operations, Windreich Department of AI & Human Health and HPI.MS@Mount Sinai
- Peter Schuffler, Dr. sc., Professor at TUM
- Ida Häggström, PhD, Professor at Chalmers University
- Luke Geneslaw, Sr Project Manager at MSKCC
- Dig Vijay Kumar Yarlagadda, Graduate Student at GSK
- David Ho, PhD, Machine Learning Scientist at MSKCC
- Andrew Schaumberg, PhD, Research Scientist at Veterans Affairs Boston Healthcare System
- Hassan Muhammad, PhD, Principal AI Scientist at PathomIQ
- Chensu Xie, PhD, Principal AI Scientist at PathomIQ
- Chao Feng, PhD, GenMap
- John McCance – Intern
- Evan Merzon – Intern
- Elham Ghelichkhan – Intern
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