Team: MinesParisTech, PSL Research University, Center for Mathematical Morphology and CBIO-Centre for Computational Biology, Fontainebleau, France
Authors: Peter Naylor, Vaia Machairas, Etienne Decenciere, and Thomas Walter
In this paper, we present our method to address the two tasks of the challenge CAMELYON16: tumor detection and slide classification. Unless specified, all steps are performed at resolution 2 of slides images. Our method is based on supervised pixel classification, where features are calculated on superpixel supports of different scales. For classification, we tested Random Forests and Support Vector Machines.
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.
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