PRET is a few-shot system for pan-cancer recognition without example training

Yi Li; Ziyu Ning; Tianqi Xiang; Qixiang Zhang; Zhihao Lin; Min Yi; Feiyan Feng; Baozhen Zeng

cs.CVOct 18, 2026

PRET is a few-shot system for pan-cancer recognition without example training

Authors: Yi Li, Ziyu Ning, Tianqi Xiang, Qixiang Zhang, Zhihao Lin, Min Yi, Feiyan Feng, Baozhen Zeng,

Organizations: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China. · Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. · Department of Pathology, the Fifth People’s Hospital of Qinghai Province, Qinghai Province Cancer Hospital, Xining, China. · Institute of Pathology and Southwest Cancer Center, the First Affiliated Hospital (Southwest Hospital) and School of Basic Medical Sciences, Army Medical University (Third Military Medical University) and the Key Laboratory of Tumor Immunopathology, The Ministry of Education (Third Military Medical University), Chongqing, China. · Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. · Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA. · Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA. · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA. · Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau SAR, China. · Department of General Surgery, Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital; Clinical Data Science Institute, Wenzhou Medical University, Wenzhou, China. · Department of Hepatobiliary Surgery, Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital; Clinical Data Science Institute, Wenzhou Medical University, Wenzhou, China. · Guangzhou National Laboratory, Guangzhou, China. · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China. · Shenzhen-Hong Kong Collaborative Innovation Research Institute, The Hong Kong University of Science and Technology, Shenzhen, China. · Shenzhen Loop Area Institute, Shenzhen, China.

Abstract

Pathological examination stands as the cornerstone in cancer diagnosis, impacting millions worldwide annually. With the shortage of pathologists globally, artificial intelligence (AI) has emerged rapidly to automate the diagnostics process. However, conventional AI models require substantial labeled data for each disease, posing huge challenges in scalability and practicality. Therefore, we introduce PRET (pan-cancer recognition without examples training), a few-shot system to achieve flexible, scalable, and effective cancer recognition across diverse organs, hospitals and tasks without training. Evaluated on 23 international benchmarks comprising 4,484 whole-slide images, our method outperforms existing approaches across 20 tasks, achieving over 97% area under the curve on 15 benchmarks with a maximum improvement of 36.76%. Notably, PRET delivers clinical-grade diagnostic performance in lymph node metastasis detection using only eight slide examples, outperforming 11 pathologists. By offering a flexible and cost-effective solution for pan-cancer recognition, PRET paves the way for accessible and equitable AI-based pathology systems, particularly benefiting minority populations and underserved regions.

Computational Pathology

Computational pathology uses algorithms to analyze digitized tissue images for diagnosis, prognosis, biomarker discovery, and clinical decision support.

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Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization

Tianyang Wang, Ziyu Su, Abdul Rehman Akbar, Usama Sajjad+5

Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA · Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA

Pathology Foundation Models Oncology Histopathology Image Classification Medical Image Representation Learning Whole-Slide Image Analysis

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The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified slide-level reasoning and interpretable linkage to clinically meaningful information. We present ASTRA, a pan-cancer framework that integrates heterogeneous foundation-model representations into a shared slide-level representation space and semantically grounds that space using structured pathology annotation fields, including classification category, cancer type, and anatomic site. ASTRA combines sparse mixture-of-experts contextualization, masked multi-model reconstruction, and contrastive alignment to structured pathology prompts to learn slide representations that support 4-category classification, 3-class solid tumor typing, 16-class cancer typing, and text-guided tumor localization without pixel-level supervision. Developed on a CHTN cohort of 10,359 whole-slide images (WSIs) spanning 16 tumor types, ASTRA consistently improves pan-cancer classification across four pathology foundation-model backbones, achieving up to 97.8% macro-AUC for 4-category classification, 99.7% for 3-class solid tumor typing, and 99.2% for 16-class cancer typing. For tumor localization, ASTRA achieves a mean Dice of 0.897 on an annotated in-domain CHTN subset (n = 380) spanning 16 cancer types and 0.738 on an external TCGA cohort (n = 1,686) spanning four cancer types. These results demonstrate that minimal structured pathology annotation fields derived from slide-level metadata can provide effective semantic supervision for unified slide representation learning, enabling both pan-cancer prediction and weakly supervised tumor localization within a single framework.

Abstract

Computational Pathology

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