Quincy Gu, PhD

As an Assistant Professor in Computational Pathology & AI Center of Excellence (CPACE) within the Department of Pathology at University of Pittsburgh School of Medicine (UPSOM), Quincy Gu, Ph.D., focuses his research on developing and applying artificial intelligence approaches to transform digital pathology. His work bridges the critical gap between clinical practice and computational analysis through innovative and interpretable AI frameworks.

Dr. Gu has made significant contributions to computational pathology by co-leading the development of several innovative AI platforms, including but not limiting to platforms for whole slide imaging analysis and automated pathology report generation at CPACE. His pioneering works also include creating interpretable deep neural networks for cancer classification, developing unsupervised anomaly detection approaches for tumor segmentation, and establishing novel AI frameworks for virtual staining. As the Associate Director of Research and Development (R&D) at CPACE, he oversees projects/products aimed at enhancing diagnostic and prognostic capabilities through computational approaches to histopathology.

Dr. Gu is dedicated to education and training the next generation of computational pathology experts. He serves as co-instructor for “Computation and Biology” in the Bioinformatics and Computational Biology (BICB) Graduate Program at the University of Minnesota (UMN) and has delivered guest lectures on courses including “Systems and Computational Physiology”, and “Entrepreneurship and Leadership Seminar” at UMN. He has organized and presented numerous workshops and tutorials on digital pathology at national conferences. He is actively mentoring undergraduate students, master and Ph.D. students, medical students, postdoctoral fellows, data scientists and software/machine learning(ML) engineers, fostering interdisciplinary collaboration and innovation.

Dr. Gu serves on the Artificial Intelligence Committee of the College of American Pathologists (CAP), contributing to national and/or international efforts in designing innovative AI strategy, establishing AI standards for pathology practice, and collaborating with non-governmental organizations as well as industry partners to validate AI systems and use cases in pathology. Dr. Gu also serves as Co-Chair of the Digital and Computational Pathology Scientific Interest Group (DCP SIG) at the American Society of Investigative Pathology (ASIP), contributing to advancing the utilization of physical, mathematical, and biophysical techniques for disease research, fostering cross-disciplinary collaboration between pathologists and investigators in computational imaging and biophysical approaches, and promoting translational research initiatives that bridge mechanistic discoveries with clinical diagnostic applications. He is an active member of the Association of Pathology Informatics (API), where he serves on the Digital Pathology and Artificial Intelligence Workgroup Committee and the Technology Standards and Innovation Committee, helping shape the future direction of computational approaches in pathology. He is also an active leader within the Digital Pathology Association (DPA), where he serves on multiple committees, including Education, Membership, and Creative Content Committees, as well as specialized working groups focused on AI curriculum developments, DPA Podcast initiatives, etc. His editorial contributions include serving as lead guest editor for a special issue on “Guidelines, Best Practice Frameworks, and Standardization in Digital Pathology”, as well as the editorial board member for the Journal of Histotechnology, and as a reviewer for Journal of Pathology Informatics, American Journal of Pathology, Modern Pathology, etc.

Of special interest are his ongoing collaborations with clinicians, where Dr. Gu is pioneering interpretable feature-based networks for understanding of tumor microenvironment and integrating multimodality learning to correlate histologic features with molecular data. His work has resulted in multiple patents in Artificial Intelligence for digital pathology and numerous peer-reviewed publications in prestigious journals. Through these efforts, Dr. Gu is helping transform how pathologists interact with digital tools, ultimately enhancing patient care through more precise and explainable computational diagnostics.

Focus Areas

• Application Development. Dr. Gu co-leads R&D initiatives at CPACE, where he oversees the creation of cutting-edge computational tools for clinical pathology. His portfolio encompasses advanced medical imaging analytics, generative AI solutions for pathology report automation, and sophisticated multimodality learning systems that integrate medical images with clinical reports and molecular pathology datasets.
   
• Whole Slide Imaging Analysis. At the core of Dr. Gu's research is the development of explainable computational methodologies that enhance cancer diagnostics through whole slide image (WSI) analysis. His innovations extend to the frontier of synthetic WSI generation and virtual staining techniques, leveraging state-of-the-art generative AI approaches to transform traditional pathology workflows.
   
• Multimodality Learning & Biomarker Discoveries. Dr. Gu has pioneered quantitative pathology features (QPFs) extraction and interpretable deep learning (DL) frameworks that elucidate the cell-of-origin (COO) classification in diffuse large B-cell lymphoma (DLBCL). His collaborative research with industry partners focuses on integrating molecular pathology data with histopathological imagery to advance cancer prognostic studies and facilitate novel biomarker discovery—bridging the gap between visual tissue assessment and molecular characterization.

Research Significance to Patient Care

Dr. Gu is at the forefront of integrating artificial intelligence into pathology practice to enhance clinical diagnostics. His advanced computational frameworks can accurately detect and classify cancer in histopathology images, provide quantitative assessments of tumor characteristics, generate interpretable AI-driven pathology reports, and identify cellular features that correlate with molecular subtypes of diseases. These technologies significantly improve diagnostic precision, reduce subjective variability in pathology assessments, accelerate the diagnostic process for patients awaiting results, and help identify optimal treatment strategies based on AI-enhanced tissue analysis. His work on explainable AI models is particularly impactful for clinical implementation, ensuring that pathologists can confidently incorporate computational tools that maintain transparency while enhancing diagnostic capabilities.

Professional Highlights

• Committee Member, Artificial Intelligence Committee, College of American Pathologists (CAP), 2025-Present
• Co-Chair, Digital and Computational Pathology Scientific Interest Group (DCP SIG), American Society of Investigative Pathology (ASIP), 2025-Present
• Member, University Senate Computing and Information Technology Committee, University of Pittsburgh, 2025-Present
• Member, Technology Standards and Innovation Committee, Association of Pathology Informatics (API), 2024-Present
• Member, Digital Pathology and Artificial Intelligence Workgroup Committee, Association of Pathology Informatics (API), 2024-Present
• Member, International Immuno-Oncology Biomarker Working Group, Breast Cancer Research Foundation (BCRF), 2024-Present
• 1^st Place Award – Best Insights and Learnings, Roche Creator Event, 2023
• University of Minnesota Graduate School Doctoral Dissertation Fellowship, University of Minnesota – Twin Cities, 2022-2023
• 1^st Place Hackathon Award, Roche CSI-RIS Hackathon, 2022

Appointments

• Assistant Professor, Department of Pathology, University of Pittsburgh School of Medicine
• Associate Director of R&D, Computational Pathology & AI Center of Excellence (CPACE), Department of Pathology, University of Pittsburgh School of Medicine
• Associate Member, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center

Education

• Ph.D. – Biomathematics, Bioinformatics and Computational Biology (2019-2023)
  University of Minnesota – Twin Cities/Rochester
• Predoctoral – Biochemistry and Molecular Biology (2019-2023)
  Mayo Clinic Graduate School of Biomedical Sciences
• Bachelor – Mathematics (2016-2019)
  University of Minnesota – Twin Cities/Rochester