Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists

Lizhi Shao,Zhenyu Liu,Ye Yan,Jiangang Liu,Xiongjun Ye,Haizhui Xia,Xuehua Zhu,Yuting Zhang,Zhiting Zhang,Huiying Chen,Wei He,Cheng Liu,Min Lu,Yi Huang,Kai Sun,Xuezhi Zhou,Guanyu Yang,Jian Lu,Jie Tian

IEEE Transactions on Biomedical Engineering（2021）

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摘要

The grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis and treatment system of prostate cancer. End-to-end method for stratifying the patient-level pathological appearance of prostate cancer (PCa) in magnetic resonance (MRI) are of high demand for clinical decision. Existing methods typically employ a statistical method for integrating slice-level re...

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关键词

Magnetic resonance imaging,Principal component analysis,Pathology,Lesions,Optimization,Task analysis,Predictive models

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