Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images.

Immunohistochemistry (IHC) plays an essential role in breast cancer diagnosis and treatment. Reliable and automatic segmentation of breast cancer regions on IHC images would be of considerable value for further analysis. However, the prevalent fully convolutional networks (FCNs) suffer from difficulties in obtaining sufficient annotated training data. Active learning, on the other hand, aims to reduce the cost of annotation by selecting an informative and effective subset for labeling. In this paper, we present a novel deep active learning framework for breast cancer segmentation on IHC images. Three criteria are explicitly designed to select training samples: dissatisfaction, representativeness and diverseness. Dissatisfaction, consisting of both pixel-level and image-level dissatisfaction, focuses on selecting samples that the network does not segment well. Representativeness chooses samples that can mostly represent all the other unlabeled samples and diverseness further makes the chosen samples different from those already in the training set. We evaluate the proposed method on a large-scale in-house breast cancer IHC dataset and demonstrate that our method outperforms the state-of-the-art suggestive annotation (SA) [1] and representative annotation (RA) [5] on two test sets and achieves competitive or even superior performance using 40% of training data to using the full set of training data.