A Novel Two-Stage Deep Method for Mitosis Detection in Breast Cancer Histology Images

The accurate detection and counting of mitosis in breast cancer histology images is very important for computer-aided diagnosis, which is manually completed by the pathologist according to her or his clinic experience. However, this procedure is extremely time consuming and tedious. Moreover, it always results in low agreement among different pathologists. Although several computer-aided detection methods have been developed recently, they suffer from high FN (false negative) and FP (false positive) with simply treating the detection task as a binary classification problem. In this paper, we present a novel two-stage detection method with multi-scale and similarity learning convnets (MSSN). Firstly, large amount of possible candidates will be generated in the first stage in order to reduce FN (i.e., prevent treating mitosis as non-mitosis), by using the different square and non-square filters, to capture the spatial relation from different scales. Secondly, a similarity prediction model is subsequently performed on the obtained candidates for the final detection to reduce FP, which is realized by imposing a large margin constraint. On both 2014 and 2012 ICPR MITOSIS datasets, our MSSN achieved a promising result with a highest Recall (outperforming other methods by a large margin) and a comparable F-score.

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