Manifold reconstructed semi-supervised domain adaptation for histopathology images classification

Abstract

Deep learning has been applied in computer-aided whole slide images diagnosis widely. However, most deep networks require lots of labeled samples, which is time-consuming and laborious. Meanwhile, most existing methods only extract the deep abstract features only, ignoring the low-level features, which contain the cell structure information. To solve the problems mentioned above, a manifold reconstructed semi-supervised domain adaptation model is proposed for whole slide images’ classification. First, a transferred network BreNet is proposed for extracting features from multiple layers of source and target domains. Multi-level features are fused and aligned to characterize the cell structure of the target domain. A novel manifold reconstructed domain adaptation method is proposed to obtain the low-dimensional embedding of the fused features and minimize the cross-domain discrepancy simultaneously. The patch-level prediction probabilities are aggregated for final image-level classification. The experimental results evaluated on three breast cancer datasets indicate the potential of the proposed method for histopathology classification in clinical setting.