The 8th International Symposium on Image Computing and Digital Medicine (ISICDM 2025) will be held at Shenzhen University from December 20 to 22, 2025. This year, ISICDM will feature a sub-forum on “Empowering Precision Medicine through Intelligent Digital Pathology,” which will bring together top scholars from around the world to discuss the latest research and applications of intelligent digital pathology in precision medicine.

Overview

With the rapid advancement of artificial intelligence, digital and intelligent pathology is becoming a core driver of precision medicine. By integrating high-resolution pathological images with multi-omics data, and leveraging cutting-edge technologies such as large AI models and multimodal fusion, intelligent digital pathology enables quantitative analysis of microscopic disease features, significantly improving the accuracy and efficiency of pathological diagnosis. This provides essential support for personalized treatment and prognosis evaluation.

However, real-world implementation still faces critical challenges — including model interpretability & transparency, patient privacy protection, and ethical compliance.

This forum will bring together prominent experts from pathology, AI, clinical medicine, and bioinformatics to discuss:

• Breakthroughs in frontier technologies
• Clinical application scenarios
• Standardization and regulatory considerations

Through interdisciplinary dialogue, the forum aims to connect technological innovation with clinical needs, driving the paradigm shift from experience-based medicine to data-driven medicine, and contributing new momentum to the global precision medicine ecosystem.

Forum Agenda

Chairs

Assistant Professor (Research) at the Departments of Computer Science & Engineering, Chemical & Biological Engineering, and Division of Life Science; Director of the Joint Innovation Center for Med-Engineering Crossover.

• Research interests: large medical foundation models, computational pathology, multimodal data fusion, medical image analysis, interpretable deep learning, AI-assisted minimally invasive diagnosis.
• Published over 200 papers in Nature Biomedical Engineering, Nature Communications, The Lancet Digital Health, Nature Machine Intelligence, JAMA, MICCAI, IEEE Transactions on Medical Imaging, Medical Image Analysis, CVPR, and ICCV, with more than 35,000 Google Scholar citations and a Nature Index of 79.
• Recognized as Stanford University Top 2% Scientist (multiple years) and Clarivate Highly Cited Researcher.
• Awards: 2023 Asian Young Scientist Award, Ministry of Education (China) Scientific Research Excellence Award (2nd Prize), Beijing Science & Technology Progress Award (1st Prize), MICCAI 2019 Young Scientist Award.
• Serves on editorial boards of IEEE RBME, IEEE TMI, IEEE TNNLS, JBHI, and CMIG; Area Chair for ICLR, CVPR, ACM MM, and MICCAI; led teams to over 15 international medical image analysis challenge championships.

Professor at the School of Informatics and jointly appointed Professor at the School of Medicine; Vice Director of the Digital Fujian Institute of Big Data for Health; Director of the XMU Medical AI Research Institute; Chair of MICS; Vice Chair of the AI Group of the Radiology Branch of the Fujian Medical Association; PhD from the Chinese University of Hong Kong.

• Research focus: medical image processing, AI-assisted diagnosis.
• Published over 120 papers in Nature Machine Intelligence, Nature Communications, IEEE Transactions on Medical Imaging, Medical Image Analysis, CVPR, AAAI, etc.
• PI/Co-PI for NSFC instrumentation projects, China’s “Innovation 2030” Megaprojects, National Key R&D Program, and NSFC general/youth projects.
• Awards include Tencent Rhino-Bird Research Award, Fujian Provincial Science & Technology Progress Award (2nd Prize), 2023 Tian Zhaowu Interdisciplinary Research Award (1st Prize) at XMU.
• Led teams to win 11 international medical imaging competitions.

Vice Dean of the School of Artificial Intelligence; Level-II Professor; Executive Director of the Institute of Smart Healthcare.

• PhD from Zhejiang University; postdoctoral researcher and visiting professor at Rutgers University and Case Western Reserve University.
• Roles: Deputy Director of the Medical Image Analysis Committee of the Jiangsu AI Society; Member of the Digital Pathology & AI Committee of the Chinese Society of Pathology; rotating Chair of the 4th Youth Symposium on Medical Image Computing.
• Published in Nature Communications, Radiology, IEEE Transactions on Medical Imaging, Medical Image Analysis; listed as Stanford University Top 2% Scientist.
• PI for NSFC joint key projects, NSFC general projects, National Key R&D Program key projects, and provincial/ministerial grants.
• Research focuses: medical image computation, computational pathology, quantitative analysis of imaging and pathological slices for disease classification, risk prediction, diagnosis, treatment response, and prognosis evaluation.

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Keynote Speakers & Topics

Talk: Digital Pathology & Artificial Intelligence: Current Status and Future Perspectives
Abstract: Diagnostic pathology is the foundation and gold standard for cancer identification, but high inter-observer variability significantly impacts diagnostic consistency and efficiency, particularly in areas with shortages of pathologists. Despite rapid developments in computer-aided diagnosis (CAD), whole-slide pathology diagnosis still faces significant challenges in real-world settings. This talk reviews the latest progress in digital pathology, presents AI algorithms and solutions developed by our team to address practical challenges, and discusses open questions hindering wider clinical adoption in China.
Bio: From 2009–2011, served as Assistant Professor at Rutgers University (Departments of Pathology, Radiology, and Biomedical Engineering). From 2011–2014, Assistant Professor at the University of Kentucky. From 2014–2019, Preeminence Hire at the University of Florida with tenure as Associate Professor across three departments. Since 2020, Professor at Westlake University focusing on AI, medical imaging, machine learning, computer vision, and medical foundation models. Author of 100+ publications in Nature Machine Intelligence, Nature Medicine, CVPR, ECCV with over 10,000 citations. Winner of MICCAI Young Scientist Awards (2015, 2024), ECCV 2024 Best Paper Finalist; listed in Stanford University’s Top 2% Scientists.

Talk: Whole-Slide Pathology Scanning and Computational Analysis
Abstract: Histopathology imaging and computational analysis are the clinical gold standards for cancer diagnosis. Current imaging technologies suffer from low speed and precision; manual diagnosis is labor-intensive and prone to subjective bias, potentially resulting in missed or incorrect diagnoses. This talk introduces the evolution of computational pathology, our lab’s work on high-speed whole-slide scanning and AI-assisted computational diagnosis, and future development prospects.
Bio: Professor and Doctoral Supervisor; recipient of the National Science Fund for Excellent Young Scholars. Has authored 100+ publications in Nature sub-journals, IEEE Transactions, NeurIPS, CVPR, ICCV; holds over 50 patents. Winner of the China National Science & Technology Progress Award (2nd Prize); recognized as Guangdong Province Science & Technology Innovation Leading Talent.

Talk: Multi-Instance Learning with Bag and Instance-Level Optimization for Digital Pathology Diagnosis
Abstract: Digital pathology diagnosis is typically framed as a multi-instance learning (MIL) classification task supervised only by bag-level labels, often neglecting instance classification. Accurate instance classification is vital for interpretability and biomarker discovery; furthermore, it can improve bag-level classification performance. This talk presents our research on optimizing both bag and instance classification, including works published at NeurIPS and ICCV.
Bio: Professor at the Digital Medicine Center of Fudan University; Deputy Director of the Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention. Research areas: AI-based digital pathology, medical imaging AI, and computer-aided drug screening. Author of 80+ papers in IEEE TPAMI, IEEE TMI, BIB, NeurIPS, ICML, CVPR, ICCV; recipient of China National Technology Invention Award (2nd Prize), Shanghai Youth Science & Technology Talent, and Shanghai Outstanding Technical Leader honors.

Talk: Key Instance Mining: Identifying Local Diagnostic Cues in Pathological Images
Abstract: Addresses the visual redundancy of ultra-high-resolution whole-slide images (WSIs) using a multi-instance learning (MIL) framework to study key instance evaluation and selection. Models the weakly supervised relationship between image patches and diagnostic labels, employing attention mechanisms, local pseudo-classifiers, and clustering strategies to identify discriminative local cues. Promotes interpretability by transitioning from global to local evidence in AI-assisted pathology analysis.
Bio: Professor at the School of Big Data & Software Engineering, Chongqing University; Vice Director of the Big Data Intelligence Research Institute; ACM Chongqing Rising Star Awardee. Research focuses on medical image processing, open-world pattern recognition, and intelligent industrial inspection. Author of 60+ publications in IEEE TIP, TIFS, TNNLS, TMI, ICCV, CVPR, AAAI, IJCAI; serves as Area Chair for IJCAI, PRCV; executive committee member for multiple academic societies.

Talk: Instance Segmentation in Pathology Images and Clinical Applications
Abstract: Accurate instance-level segmentation of pathological structures such as nuclei and glands from digital pathology images is a key task in computational pathology. It underpins many clinical applications including tumor microenvironment analysis and precise immunohistochemistry interpretation, yet faces challenges such as labor-intensive annotation, strong heterogeneity, and structural complexity. This talk is divided into three parts: (1) overview of challenges, solutions, and current research status in pathology instance segmentation; (2) our recent work under weak annotation settings, including few-shot learning, semi-supervised learning, and approaches based on vision-language large models; (3) clinical use cases in related technologies.
Bio: Professor at the School of Automation, South China University of Technology; “Pearl River Talent Plan” Distinguished Young Scholar of Guangdong Province. Has studied at Xi’an Jiaotong University, Huazhong University of Science and Technology, and University of Nebraska–Lincoln (USA). Focuses on AI algorithms and translational applications for pathology. First/corresponding author of 40+ papers in TMI, MIA, MICCAI, TIP, CVPR; PI of 10+ national/provincial projects; first inventor on 12 patents worth nearly RMB 5 million in completed tech transfers; AI pathology products have been deployed in multiple hospitals. Academic roles include Deputy Secretary-General of the Visual Cognition & Computing Committee of CSIG and MICS Executive Committee Member.

Talk: Multi-Level Representation Learning for Whole-Slide Images and Tumor-Aided Diagnosis
Abstract: Whole-slide image (WSI) representation learning is central to digital pathology analysis and tumor-aided diagnosis. This talk introduces a multi-level representation learning framework spanning from the microscopic level of cells to the macroscopic level of tissues, as well as integration with cross-case multimodal structured learning. The framework effectively improves tumor screening, molecular status prediction, and prognosis evaluation across multiple auxiliary diagnostic systems.
Bio: Associate Professor at Beihang University, focusing on digital pathology image processing and tumor-aided diagnosis. PI of over 10 projects including National NSFC, Beijing Natural Science Foundation, and industry-academia collaborations. Author of 50+ papers in IEEE TMI, Medical Image Analysis, AAAI, MICCAI; holder of 10+ patents. Recognitions include the Young Scientist Award from the Chinese Society for Stereology and Biomedical Engineering.

Talk: Efficient Computation and Processing of Digital Pathology Images
Abstract: Computational pathology can assist in precise cancer diagnosis and treatment, but the extremely high resolution of pathology images poses challenges for annotation and computation. To address low annotation efficiency, our team developed multi-task learning, weak supervision, and self-supervised learning strategies that greatly reduce the need for manual expert labeling. To improve computational efficiency, lightweight network models and knowledge distillation were employed to enable large-scale, rapid pathology image analysis. This work provides core technical support for building efficient, automated intelligent pathology diagnostic systems.
Bio: Guangdong Distinguished Young Scholar; PhD in Computer Science from The Chinese University of Hong Kong; Associate Researcher and PhD Supervisor at Guangdong Provincial People’s Hospital; PI at the Guangdong Key Laboratory for Intelligent Medical Imaging Analysis & Application. Research focuses on AI algorithms for oncology imaging and computational pathology. Author of 70+ papers in TPAMI, TNNLS, TMI, MedIA, ACM TOG, CVPR, MICCAI; filed 20+ patents (6 granted). PI for multiple NSFC and Guangdong grants; awards include Guangdong S&T Progress Award (1st Prize, 2024) and 2023 National Digital Health Application Contest Grand Prize (1st place out of 39 teams).

Talk: AI-Based Intelligent Pathology Image Analysis for Cancer-Aided Diagnosis and Treatment
Abstract: Pathological diagnosis is the gold standard in cancer treatment. AI technologies have been increasingly applied to digital pathology image analysis, reshaping cancer diagnosis and treatment. This talk focuses on our practical research in AI-assisted cancer care, including advances in histopathology image analysis and multimodal pathology data modeling for tumor classification/grading, biomarker prediction, prognosis evaluation, and therapeutic response prediction, as well as challenges and future directions.
Bio: Associate Professor at the School of Software, Hefei University of Technology; Member of MICS. Holds a PhD in Pattern Recognition & Intelligent Systems from Beihang University; former researcher at CETC 38th Research Institute. Author of 40+ papers in IEEE TMI, MIA, Journal of Pathology, MICCAI, AAAI, BIBM; inventor on 10+ patents. PI/co-PI of over 10 research projects, including NSFC and Anhui Provincial grants; reviewer for IEEE TMI, MIA, TIP, JBHI, TCSVT, MICCAI, AAAI, BIBM.

Talk: AI-Driven Smart Pathology Department Construction
Abstract: The development of smart, digital pathology departments is a hot direction globally. Artificial intelligence can significantly improve diagnostic accuracy and workflow efficiency, but real-world issues such as a wide variety of disease types and multimodal long-tail distributions create barriers. This talk shares cutting-edge industry practices in integrating segmentation, classification, and detection into pathology department operations, and highlights how multimodal large models and model production tools can address long-tail challenges in diagnosis.
Bio: PhD in Computer Science from Lehigh University (USA). Formerly with Siemens Research and Tencent AI Lab, specializing in microscopic image processing and medical AI. Over a decade of industry experience in algorithm R&D, product innovation, and quality management. Currently COO of SenseTime Medical, overseeing AI pathology and imaging product deployment and usage.

Talk: Computational Pathology Radiogenomics
Abstract: This talk presents our recent progress in intelligent pathology radiogenomics for tumor diagnosis, including tumor microenvironment component analysis, multimodal fusion, open-set active learning for nucleus detection, zero-shot tissue segmentation, image–gene association analysis, and spatial transcriptomics-based gene prediction, with applications to breast, lung, and liver cancer prognosis and treatment.
Bio: Associate Professor at the College of Artificial Intelligence, NUAA; PhD Supervisor; member of the iBrain team (headed by Prof. Daoqiang Zhang). PI of two NSFC general projects; author of 55 publications in Nature Communications (2), Cell Reports (2), IEEE TMI (12), as well as CVPR, ICCV, NeurIPS; over 3,200 Google Scholar citations. Honors include 2× MICCAI Young Scientist Awards (only mainland China recipient), 2025 MOE Natural Science Award (2nd Prize), and Stanford University Top 2% Scientist listing (2024).

About ISICDM 2025

The forum represents a unique opportunity to engage with leading experts in digital pathology and AI, explore cutting-edge research, and discuss practical implementation challenges. Through the diverse expertise of our distinguished speakers, participants will gain comprehensive insights into the latest technological advances and their clinical applications. We warmly welcome researchers, clinicians, and industry professionals to join us in shaping the future of intelligent digital pathology.