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. · 

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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.

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