October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

Posted on by Tarissa Arnold Mages

The integration of Artificial Intelligence (AI) in clinical pathology has faced significant hurdles due to constraints in data collection and challenges associated with model transparency and interpretability.  In this talk, we introduce a novel digital pathology AI framework named nuclei.io, which leverages active learning and incorporates real-time feedback from human experts. This innovative approach empowers pathologists to quickly generate diverse datasets and develop models for various clinical applications. To demonstrate the effectiveness of our framework, we conducted two user studies employing such human–AI collaboration strategy. These studies focused on two key areas: the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. The results from these studies showed significant enhancements in sensitivity, accuracy, and diagnostic efficiency with the integration of AI. Our findings underscore the benefits of the human-in-the-loop AI framework, highlighting its potential to transform the field of digital pathology.

Speaker:  Zhi Huang, PhD
Affiliation: Perelman School of Medicine, University of Pennsylvania
Position:  Assistant Professor (incoming), Dept of Pathology and Laboratory Medicine, Dept of Biostatistics, Epidemiology and Informatics
Research Links: https://www.zhihuang.ai
Host: Andrew Song, PhD – Mahmood Lab

Date: Monday, October 21, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Zhi Huang is an incoming tenure-track assistant professor at the University of Pennsylvania starting January 2025. He obtained his PhD in Electrical and Computer Engineering (ECE) from Purdue University in August 2021. Since August 2021, He has been a postdoctoral fellow at Stanford University. His research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology, human-AI collaboration, neurodegenerative diseases, etc. His research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, Zhi Huang co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology. It was selected as one of only 9 Stanford Catalyst 2023 cohort innovations.

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ABC Seminar: Zhi Huang, PhD – University of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

Speaker:  Zhi Huang, PhD
Affiliation: Perelman School of Medicine, University of Pennsylvania
Position:  Assistant Professor (incoming)
Dept of Pathology and Laboratory Medicine
Dept of Biostatistics, Epidemiology and Informatics
Host: Andrew Song, PhD – Mahmood Lab

Date: Monday, October 21, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Abstract: The integration of Artificial Intelligence (AI) in clinical pathology has faced significant hurdles due to constraints in data collection and challenges associated with model transparency and interpretability.  In this talk, we introduce a novel digital pathology AI framework named nuclei.io, which leverages active learning and incorporates real-time feedback from human experts. This innovative approach empowers pathologists to quickly generate diverse datasets and develop models for various clinical applications. To demonstrate the effectiveness of our framework, we conducted two user studies employing such human–AI collaboration strategy. These studies focused on two key areas: the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. The results from these studies showed significant enhancements in sensitivity, accuracy, and diagnostic efficiency with the integration of AI. Our findings underscore the benefits of the human-in-the-loop AI framework, highlighting its potential to transform the field of digital pathology.

Research Links: https://www.zhihuang.ai

Zhi Huang is an incoming tenure-track assistant professor at the University of Pennsylvania starting January 2025. He obtained his PhD in Electrical and Computer Engineering (ECE) from Purdue University in August 2021. Since August 2021, He has been a postdoctoral fellow at Stanford University. His research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology, human-AI collaboration, neurodegenerative diseases, etc. His research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, Zhi Huang co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology. It was selected as one of only 9 Stanford Catalyst 2023 cohort innovations.

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ABC Seminar: Prateek Prasanna, PhD – Stony Brook University – “Reality-aware Medical Vision: The Quest for Interpretable and Explainable AI in Medical Imaging”

Prateek Prasanna, PhDSpeaker:  Prateek Prasanna, PhD
Affiliation: Stony Brook University
Position:  Assistant Professor, Department of Biomedical Informatics
Host: Daniel Shao, Mahmood Lab

Date: Monday February 24th, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Abstract: Medical imaging plays a critical role in modern healthcare, yet its complexity necessitates advanced computational tools to enhance analysis, interpretability, and diagnostic accuracy. In this presentation, we will discuss our research efforts in developing computational imaging biomarkers and frameworks for precision medicine in real clinical scenarios involving imperfect data. We will cover a spectrum of computational techniques grounded in both biological and domain-specific insights that facilitate the early detection and evaluation of treatment responses across various diseases.

In radiology, we have developed methods to analyze intricate tissue structures using radiomics and topology-informed deep learning, demonstrating improved prediction of treatment response in multiple cancers. Additionally, we explore techniques that integrate expert eye gaze patterns and radiomic features to condition diffusion models, enabling disease-aware image synthesis and enhancing the generation of anatomically accurate and clinically relevant medical images. These approaches provide both diagnostic accuracy and interpretability, bridging the gap between AI models and clinical needs.

In digital pathology, our work focuses on overcoming challenges such as domain shifts, data scarcity, and limited interpretability. We employ adaptive meta-learning frameworks to generalize across unseen staining patterns, improving segmentation and classification of histopathology images. Generative models are utilized to synthesize high-resolution pathology images and restore critical details, leveraging pathology text reports and handcrafted features to enhance both quality and interpretability. Finally, we integrate expert pathological insights with deep learning to improve interpretability in whole-slide image analysis, demonstrating the potential to provide meaningful explanations alongside robust performance.

These advancements highlight the transformative potential of explainable and generative AI in radiology and digital pathology, paving the way for innovative, clinically relevant solutions in medical imaging.

Research Links: Publications, IMAGINE Lab

Prateek Prasanna is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University and directs the Imaging Informatics for Precision Medicine (IMAGINE) Lab. He received his PhD in Biomedical Engineering from Case Western Reserve University, Ohio, USA. Prior to that, he obtained his master’s degree in Electrical and Computer Engineering from Rutgers University and bachelor’s degree in Electrical and Electronics Engineering from National Institute of Technology, Calicut, India. Dr. Prasanna’s research focuses on building clinically translatable machine learning tools that leverage multiple data streams of imaging, pathology, and genomics to derive actionable insights for enabling better treatment decisions. His research involving development of companion diagnostic tools for thoracic, neuro, and breast imaging applications has been published in venues such as MICCAI, CVPR, ECCV, NeurIPS, Radiology, Medical Image Analysis, etc, and has won several innovation awards. One of the core focuses of his lab is to integrate machine generated inferences with expert clinical reads to make clinical workflows more efficient and effective. His team has been actively working on the advancement of interpretable machine learning and xAI techniques to facilitate the discovery of computational biomarkers, particularly in situations where data is limited or missing.

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