Cen Shi

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OBJECTIVES To evaluate the potential value of advanced monoenergetic images (AMEIs) on early gastric cancer (EGC) using dual-energy CT (DECT). METHODS 31 EGC patients (19 men, 12 women; age range, 38-81 years; mean age, 57.19 years) were retrospectively enrolled in this study. Conventionally reconstructed polyenergetic images (PEIs) at 120 kV and virtual(More)
Multiple instance learning algorithms have been increasingly utilized in computer aided detection and diagnosis field. In this study, we propose a novel multiple instance learning method for the identification of tumor invasion depth of gastric cancer with dual-energy CT imaging. In the proposed scheme, two level features, bag-level features and(More)
Feature selection and classification are important tasks in medical data mining. However, different misclassifications of medical cases could lead to different losses. This paper proposes a framework for medical data classification and relevant feature selection by the combination of the trace ratio criterion and a novel cost-sensitive linear discriminant(More)
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