4/11/2023 0 Comments Machine learning least squares![]() Seven abdominal CT exams were retrospectively collected from the same type of CT scanners. The internal noise component was incorporated to model the inefficiency and variability of human observer (HO) performance, and to generate the ultimate DL‐MO test statistics. The PLSR model was used to further engineer the deep feature for the lesion detection task in CT images. ![]() The earlier layers of the CNN were used as a deep feature extractor, with the assumption that similarity exists between the CNN and the human visual system. ![]() The CNN was previously trained to achieve the state‐of‐the‐art classification accuracy over a natural image database. The DL‐MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS‐DA) model and an internal noise component.
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