The Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.
This seminar features Dr Peixian Liang and Songhao Li from the University of Pennsylvania, USA.
Abstract
Computational pathology is rapidly moving beyond static, single-task predictors toward interactive systems that can segment, reason, and quantify tissue morphology under expert supervision. In this talk, we present two complementary pillars of this transition from our group. First, we introduce VISTA-PATH, an interactive foundation model for histopathology image segmentation designed to resolve heterogeneous tissue structures, incorporate expert feedback, and generate pixel-level segmentations that are directly meaningful for clinical interpretation. Trained on over 1.6 million image–mask–text triplets spanning 9 organs and 93 tissue classes, VISTA-PATH outperforms existing segmentation foundation models and enables clinically relevant biomarkers, such as the Tumor Interaction Score (TIS), which shows a strong association with patient survival. Second, we present TissueLab, an open, co-evolving agentic system (tissuelab.org) for end-to-end medical image analysis. TissueLab unifies foundation models and analytical tools within a modular tool graph, including VISTA-PATH for interactive segmentation, and translates LLM-generated plans into executable operations. Through tool-level, strategy-level, and workflow-level co-evolution, it achieves over 90% accuracy in cancer cell quantification within 10-30 minutes of expert feedback, improves lymph-node metastasis correlation from 0.827 to 0.933, and reaches a macro-AUC of 0.838 on tubule formation scoring, outperforming human-orchestrated workflows and state-of-the-art VLMs.
Bio
Peixian Liang is a Postdoctoral Researcher at the University of Pennsylvania, working with Prof. Zhi Huang. She received her Ph.D. degree in Computer Science from the University of Notre Dame, Notre Dame, IN, USA, in 2023. Her research focuses on developing innovative AI/ML methods to advance medicine. She is particularly interested in building foundation models and computational frameworks to assist pathologists, reduce variability in diagnosis, and identify clinically relevant morphologic biomarkers. Her work aims to advance AI-driven approaches for more precise diagnosis, prognosis, and clinical and biological discovery. Songhao Li is a first-year Ph.D. student in Electrical and Systems Engineering at the University of Pennsylvania, advised by Prof. Zhi Huang. He received his M.S. in Computer Science from New York University in 2025 and brings a strong full-stack and system-engineering background from prior roles in industry. His research focuses on agentic AI, human–AI collaboration, and ML/DL for medicine, aiming to bridge AI research and clinical practice through transparent, interactive, and continuously learning systems.
