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

STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology

Adv Neural Inf Process Syst 2026

A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images

Comput Med Imaging Graph 2026

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

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

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

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