Prostate cancer is a highly prevalent malignancy, and deep learning has significantly advanced di agnostic models based on multi-parametric MRI (mpMRI). However, the robustness of these models is threatened by adversarial attacks, potentially leading to fatal misdiagnoses and impeding clinical translation. Unlike natur...
Yu Zhang, Jun-Qiang Qiu, Dong-Hui Li et al.· IEEE journal of biomedical a...· 0 citations
Multi-parametric magnetic resonance imaging (mpMRI) provides complementary diagnostic information; however, its clinical utility is often hindered by the issue of missing modalities resulting from prolonged scanning times, high costs, and the potential risks associated with contrast agents. While diffusion models (DMs)...
Bhatti Uzair Aslam, Xiao-Xiang Li, Wan-Ling Peng et al.· IEEE journal of biomedical a...· 0 citations
The high cost of medical image annotation severely restricts the clinical application of prostate precise multi-regional segmentation technologies. To address existing bottlenecks in semi-supervised learning methods, including insufficient alignment of local anatomical structures and pseudo-label noise accumulation, th...
Zhi-Yuan Zhang, Yu Zhang, Zi-Hao Zhou et al.· IEEE journal of biomedical a...· 0 citations
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