Su-Sampling Based Active Learning For Large-Scale Histopathology Image

Expensive annotation cost has always been a critical obstacle in deep learning systems, in particular for the applications requiring domain experts' knowledge, such as medical image analysis. Active learning has attracted widespread attention by decreasing the quantity and difficulty in annotation with query strategies. To make a query for labeling, contextual features of candidates' relation are often considered essential. In this paper, we propose an innovative method that incorporating spatial distribution approximation in the uncertainty sampling for whole-slide histopathology image annotation. The active query selection combines the measure of spatial representativeness and histopathological tissue informativeness. With the assumption that the labeling cost of individual instances is non-biased, we use three independent features, namely loss prediction query, Monte Carlo distribution query, and loss estimation enhanced by spatial information for the active learning task. The experiments were validated on large cohorts of The Cancer Genome Atlas (TCGA) and the ''100,000 histological images of human colorectal cancer and healthy tissue'' dataset. Empirically, the proposed method can outperform the other annotation strategies on the histopathology datasets. The annotation cost is reduced by an obvious margin of 40% to retain an accurate classifier. This novel annotation strategy provides the potential to efficiently label and classify histopathology images with a patch-based convolutional neural network.