Learning From Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC
Journal
Proceedings - International Symposium on Biomedical Imaging
Journal Volume
2026-April
Start Page
1
End Page
5
ISBN (of the container)
979-833157763-6
Date Issued
2026-05-20
Author(s)
Yu, Hsin-Pei
Lyu, Si-Qin
Nghiem, Van Quang
Hsieh, Yi-Hsien
Su, Tung-Hung
Kao, Jia-Horng
Abstract
Contrast-enhanced computed tomography (CT) is recommended in clinical guidelines for managing hepatocellular carcinoma (HCC), yet complete multi-phase acquisition is often unavailable. Here, we present DuoProto, a novel dualbranch, prototype-guided framework that leverages limited multi-phase CT during training to enhance single-phase ER prediction in HCC. DuoProto aligns class-level representations across single- and multi-phase inputs via prototype learning, incorporates clinically informed ranking, and infers with single-phase only. Under conditions of class imbalance and missing phase, DuoProto consistently outperforms existing methods, achieving absolute gains of 3-9% across all metrics. The proposed framework provides a clinically aligned solution, enabling more informed decision-making. Code is available at https://github.com/idssplab/DuoProto.
Event(s)
23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Subjects
3D Medical Imaging
Early Recurrence Prediction
Prototype Learning
Publisher
IEEE
Type
conference paper