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\nTeam: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong

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\n\tAuthors: Hao Chen, Huang-Jing Lin, Qi Dou, and Pheng-Ann Heng

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\n\tAbstract:

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\n\tIn this ISBI challenge, we tackled the problem of automatic metastasis detection by a novel framework with deep cascaded networks. This united framework consists of two components integrated in a cascaded manner. \nThe first light network retrieves the candidates by scanning the whole sliding image (level 1) in a fast speed while maintaining a high sensitivity. The following fine-tuning deep neural network with powerful residual model aims at discriminating the candidates (level 0) with a high accuracy. For the post-processing, the metastasis was localized in the centers of clusters on the probability mask.\n\n

\n\tResults:

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\n\tThe following figure shows the receiver operating characteristic (ROC) curve of the method.

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\n\tThe following figure shows the free-response receiver operating characteristic (FROC) curve of the method.

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\n\tThe table below presents the average sensitivity of the developed system at 6 predefined false positive rates: 1/4, 1/2, 1, 2, 4, and 8 FPs per whole slide image.

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