University of California San Francisco – Holographic Imaging Cytometry Center of Excellence
University of California, San Francisco (UCSF) and PHI have jointly created a regional Holographic Imaging Cytometry Center of Excellence headed by Dr. Robert Judson-Torres. Located at the UCSF Helen Diller Family Comprehensive Cancer Center, the Center’s activities focus on bringing the benefits of holographic cytometry and machine-learning to the UCSF research community, by providing education and technical support.
The recurring UCSF-PHI symposium serve as a forum for scientists who worldwide develop new therapies and diagnostic techniques for melanoma and other cancers.
The speakers at the UCSF-PHI symposium 2018.
Dr. Robert Judson-Torres
Principal Investigator and a Sandler Fellow at the UCSF Department of Dermatology
Judson's research interests focus on the networks of genes and environmental factors that stabilize cell states in adult mammalian organisms, and, conversely, the coordinated sets of destabilizing factors which can lead to tumorigenesis. He is also actively involved with exploring new models of scientific training, communication and publication, including experimenting with forums for post-publication peer review, reproducibility initiatives, and strategies for training the scientific workforce.
Peer Reviewed Articles and Book Chapters
Quantification of mammalian tumor cell state plasticity with digital holographic cytometrySPIE Conference Proceedings (2018)Read more
Working with a HoloMonitor M4 digital holographic cytometry platform, we have established a machine learning-based pipeline for high accuracy and label-free classification of adherent cells.
Combined activation of MAP kinase pathway and β-catenin signaling cause deep penetrating neviNature Communications (2017)Read more
HoloMonitor was used to measure cell volume. Together with other methods the results identify DPN (deep penetrating nevus) as an intermediate melanocytic neoplasm, with a progression stage positioned between benign nevus and DPN-like melanoma.
High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cellsScientific Reports (2017)Read more
The authors used machine learning to develop a method for robust and kinetic label-free classification of single adherent cells info functional states.
Helen Diller Family Comprehensive Cancer Center
1450 3rd St, San Francisco
CA 94158, USA