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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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\nI have reproduced the algorithm of the top-performing team in the challenge event (HMS-MIT Method 1). However, instead of training the GoogLeNet from scratch, I used the pre-trained GoogLeNet model trained for ImageNet classification to initialize the weights and then finetuned the network. Similar strategies were taken to produce scores at the Image and lesion levels.

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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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