The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.
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Qi Wei, Yinhao Ren, Rui Hou, Bibo Shi, Joseph Y. Lo, Lawrence Carin, "Anomaly detection for medical images based on a one-class classification," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751M (27 February 2018);