Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (4): 284-290.doi: 10.11896/jsjkx.250600188

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Tensor-based Multimodal Fusion Technique to Diagnose Microvascular Invasion

WANG Shaodong1, LI Liujun2, LI Rui1, SU Zhongzhen2, LU Yao1   

  1. 1 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
    2 Department of Ultrasound, the Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong 519000, China
  • Received:2025-06-26 Revised:2025-09-08 Online:2026-04-15 Published:2026-04-08
  • About author:WANG Shaodong,born in 2000,postgraduate.His main research interests include deep learning and medical imaging.
    LU Yao,born in 1979,Ph.D,professor.His main research interests include medical artificial intelligence,computer-aided diagnosis,medical image analysis,machine learning,radiomics,and medical big data analysis.
  • Supported by:
    National Key R & D Program of China(2023YFE0204300),the R&D Project of Pazhou Lab(HuangPu)(2023K0606),National Natural Science Foundation of China(82441027,62371476),Guangzhou Science and Technology Bureau(2023B03J1237),Health Research Major Projects of Hunan Health Commission(W20241010) and Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University(2020B1212060032).

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Abstract: Microvascular invasion(MVI) is a critical prognostic factor for postoperative recurrence and reduced survival in hepatocellular carcinoma(HCC),making its precise preoperative localization essential for treatment planning.To address limitations of existing radiomics approaches—including poor feature generalizability,weak interpretability,and neglect of spatial peritumoral MVI distribution crucial for surgical strategies—this study proposes:1) multimodal fusion-based 3D localization of MVI via spatial alignment of whole-slide pathology images(WSIs) with 3D ultrasound(3D US);2) a feature tensor fusion deep learning model integrating multiscale features,tensor fusion,and orthogonal loss functions to extract semantic features of peritumoral MVI distribution.Model performance is evaluated on the curated dataset using metrics such as the area under the receiver operating cha-racteristic curve(AUC),Accuracy and F1 score,demonstrating its effectiveness.Experimental validation demonstrates exceptional performance(AUC:0.910,Accuracy:0.930,F1-score:0.852),confirming the proposed model’s clinical potential for precise preoperative MVI diagnosis.

Key words: Microvascular invasion, Multimodal, Tensor fusion, Multi-scale fusion, Hepatocellular carcinoma

CLC Number: 

  • TP183
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