Research Goals

Our areas of focus include the development and validation of digital pathologic and multi-modal deep learning models for:

Greater diagnostic accuracy and efficiency
 
 

Improved outcome prognostication and prediction of treatment response in cancer patient populations
 

Discovery of novel image-based biomarkers for precision medicine across various oncologic and non-oncologic diseases

Featured Publications

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types

J Immunother Cancer 2024

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

eBioMedicine 2023 

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

The Lancet Oncology 2021

Impact of a deep learning assistant on the histopathologic classification of liver cancer

NPJ Digit Med 2020

Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori

Intelligence-Based Medicine 2020

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation

IEEE Transactions on Medical Imaging 2021

Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

Scientific Reports 2021

Latest News

Join Us on December 10-11, 2024
8:00 AM - 2:00 PM PST

Please join us for the "Clinical Decision Support in Action: AI Solutions Transforming Care" session at AI+HEALTH 2024 (moderated by Dr. Jeanne Shen), where we discuss diagnostic tools for clinical decision-making across various medical specialties!

November 26,2024

Great food and fun times at our first annual Thanksgiving Lab dinner @ Tai Pan Palo Alto!

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