Toby C. Cornish

Toby C. Cornish

Professor of Pathology and Biomedical Informatics

University of Colorado School of Medicine



Toby Cornish is a Professor of Pathology and Biomedical Informatics at The University of Colorado School of Medicine, where he practices gastrointestinal pathology and clinical informatics. He was recruited to the University of Colorado (CU) in 2016 from Johns Hopkins, where he had a similar role in the Hospital and University. While on faculty at Johns Hopkins, he was Co-director for Imaging and Image Analysis of the JHU Oncology Tissue Services Lab and developed or co-developed several software packages for biomarker quantitation, including TMAJ/FrIDA, PIP, and HPASubC. In his current position, he serves as the Vice-Chair for Pathology Informatics, Medical Director of Informatics for the Pathology Department, and Medical Director of the LIS for the UCHealth system. Dr. Cornish was the 2023 recipient of the University of Colorado Department of Pathology’s Pathologist of the Year Award. In August of 2023, he was granted Tenure by the University of Colorado Board of Regents.

His interests include histologic image analysis, digital pathology, and artificial intelligence/machine learning. Dr. Cornish is a member of the HIMSS-SIIM Enterprise Imaging Community’s Multimedia Interactive Content Reporting Workgroup, the HIMSS-SIIM Enterprise Imaging Community Advisory Council Member, the College of American Pathologists Artificial Intelligence Committee and previously President of the Association for Pathology Informatics (2022). He was named to The Pathologist magazine’s Power List 2020 for Big Breakthroughs, serves on the Editorial Board for Modern Pathology, and is the Associate Editor for Informatics of the American Journal of Clinical Pathology.

Download Dr. Cornish’s curriculum vitae.

  • Clinical Informatics
  • Pathology Informatics
  • Artificial Intelligence
  • Digital Pathology
  • Computational Pathology
  • Histologic and Cytologic Image Analysis
  • Gastrointestinal Pathology
  • Pancreaticobilliary Pathology
  • Pancreatic Neoplasia
  • Clinical Informatics Certificate, 2011

    Johns Hopkins University

  • Gastrointestinal and Liver Pathology Fellow, 2010

    Johns Hopkins University

  • Anatomic Pathology Resident, 2009

    Johns Hopkins University

  • PhD in Neuroscience, 2004

    University of Illinois Urbana Champaign (UIUC)

  • MD, 2005

    University of Illinois Chicago (UIC)

  • BS in Biochemistry, 1995

    Bradley University

Recent Posts

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Recent & Upcoming Talks

Digital and Computational Pathology in Routine Diagnosis: The Nuts and Bolts of Adoption
This is an exciting time in pathology as pathology is transforming, entering into an era of rapid discoveries and technological advances based on robust advances. Next generation pathology tools, including new technological advances in anatomical pathology, are already being deployed, and many more are rapidly getting developed in research settings. The ongoing technological advances in digital whole slide imaging scanners, image visualization methods, and the integration of artificial intelligence (AI)-derived algorithms into pathology applications are already being used by some pathologists. The benefits of these tools include ease of remote access to cases via the cloud, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides, to name a few. There are advances in genomics, spatiomics, biomarker assessment, multiplexing, and other advanced imaging tools that can now provide single-cell and 3D information to anatomical pathologists. The focus of this course is to provide practical insights into the status of several technological advances in anatomical pathology, such as whole slide imaging, artificial intelligence, augmented reality, genomics, bioinformatics, multiplexing, and advanced imaging tools such as 3D rendering of pathology images. The speakers will provide insights into how close we are today to reaping the benefits of these tools and solutions for routine pathology practice. This course material will allow pathologists to be better prepared to adopt and implement these advances in their daily practice. Expert speakers in these areas will provide an overview of the complexities and barriers of implementing these tools for widespread adoption. Some of the barriers that will be highlighted include cost, technical glitches, interoperability, and, most importantly, professional hesitation to adopt new technology.

Recent Publications

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(2023). Learning with limited target data to detect cells in cross-modality images. Medical Image Analysis.

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(2023). Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings. bioRxiv.


(2023). Three-dimensional assessments are necessary to determine the true spatial tissue composition of diseased tissues. bioRxiv.


(2023). Cybersecurity and Information Assurance for the Clinical Laboratory. The Journal of Applied Laboratory Medicine.

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(2023). Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions. bioRxiv: The Preprint Server for Biology.

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