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Bo Liu

4 papers indexed

openalexBMC Medical Informatics and Decision Making2026-07-24

AI-powered spectral CT analysis for clinical decision support in carotid vulnerable plaque detection: a deep learning approach

Yunzhe Ni, Tianyu Zhang, Zonghui Huang, Yue Wang, Guochao Han, Lin Yuan, et al.

Carotid vulnerable plaques (CVPs) represent a major cause of ischemic stroke, yet current diagnostic methods lack sufficient precision for early detection. Spectral computed tomography (CT) enables detailed plaque characterization, but its clinical utility depends on advanced ana…

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openalexEnvironmental Earth Sciences2026-07-24

An enhanced methodological framework for objective assessment of regional land subsidence risks: a case study in Cangzhou city, China

Bo Liu, Yi-Xiang Wang, Shinong Li, Weiwei Li, Erjun Yu, Wenhui Cui, et al.

Regional land subsidence risk assessment is often constrained by low-resolution contour-based continuous data and subjective empirical grading, which may weaken the objectivity and spatial detail of evaluation results. To address this limitation, this study establishes an enhance…

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arxivcs.CV2026-07-07

KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning

Zheng Guo, Jiaqi Cui, Haocheng Xiong, Jize Han, Bo Liu, Qianwen Zhang, et al.

Non-invasive prediction of Gleason Grade Group (GGG) in prostate cancer using multiparametric MRI (mpMRI) is clinically vital for reducing unnecessary biopsies. Existing GGG prediction methods face two major limitations. First, they often overlook non-image information critical f…

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arxivcs.LGcs.AIcs.CR2026-07-05

One Framework for All: Cross-Modal Membership Inference for Generative Models

Dayong Ye, Tainqing Zhu, Kun Gao, Junhao Liu, Yichuan Chen, Shuai Zhou, et al.

Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inference attacks (MIA), which aim to determine whether a given data point was used in a model's traini…

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