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\nTeam: Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, USA

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\n\tAuthors: Aoxiao Zhong, Quanzheng Li

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\n\tAbstract:

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\nThis is a reproduced version of the algorithm by the top-performing team in the challenge event (HMS-MIT Method 1). I replaced the GoogLeNet they used with a deeper CNN model ResNet-101. The model is trained based on a pre-trained ResNet-101 for ImageNet classification. The classification task is based on features extracted from tumor probability map with a random forest classifier as described in the submission by HMS-MIT Method 1. Further postprocessing step was used to remove candidates that are too small for the lesion-detection task.\n\n

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\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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