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


Hao Chen, Huang-Jing Lin, Qi Dou, and Pheng-Ann Heng


In 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. The 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.


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.

FPs/WSI 1/4 1/2 1 2 4 8