Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, USAAuthors:
Aoxiao Zhong, Quanzheng LiAbstract:
I 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.Results:
The following figure shows the receiver operating characteristic (ROC) curve of the method.
The following figure shows the free-response receiver operating characteristic (FROC) curve of the method.
The 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.