KAMP-Net: Knowledge-aligned Multi-modal Prompt-tuning Network for Prostate Cancer Diagnosis from bpMRI.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
- Department of Radiology, Changshu NO.1 People's Hospital, Changshu, Jiangsu, 215501, China.
- Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China; School of Biomedical Engineering, The University of Hong Kong, Hong Kong, China.
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, Jiangsu, 215028, China. Electronic address: [email protected].
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: [email protected].
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Institute of Medical Intelligence and XR, The Chinese University of Hong Kong, Hong Kong, China.
Abstract
Bi-parametric magnetic resonance imaging (bpMRI) is widely used for non-invasive diagnosis of clinically significant prostate cancer (csPCa). Although the Prostate Imaging Reporting and Data System (PI-RADS) standardizes interpretation through structured diagnostic criteria, clinical assessment remains highly operator-dependent. Vision-language models (VLMs), such as CLIP, offer a promising automated solution by leveraging the imaging-language coupling inherent in PI-RADS. However, existing VLM adaptation methods generally treat medical diagnosis as a generic image-to-label matching task. This conventional "label-driven" paradigm relies on superficial semantic correlations, fundamentally failing to capture the structured reasoning, anatomical context, and sequence prioritization defined by clinical guidelines like PI-RADS. To address this fundamental limitation, we propose KAMP-Net, a multi-modal prompt-tuning network that shifts the paradigm toward "knowledge-driven" learning. Our core algorithmic novelty lies in mathematically translating structured PI-RADS criteria directly into the network's computational graph. Specifically, rather than employing generic feature fusion and global alignment, KAMP-Net utilizes zone-aware modality routing to dynamically gate heterogeneous bpMRI sequences, and enforces a hierarchical vision-language alignment that structurally mirrors a radiologist's bottom-up diagnostic workflow. Experiments on a multi-center cohort comprising two public and one private datasets, plus an external private dataset, show that KAMP-Net delivers competitive performance in both patient-level diagnosis and lesion-level localization. Beyond standalone performance, we evaluated its clinical utility in an AI-assisted workflow. Notably, for challenging equivocal cases (PI-RADS = 3), this human-in-the-loop strategy successfully reduces false positives without risking missed diagnoses, demonstrating its strong potential as a reliable decision-support tool. Code is available at: https://github.com/IsYuchenYuan/KAMP-Net.