Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification

Abstract

Recently, deep learning techniques achieve remarkable classification performance on histopathology images. How-ever, they usually require a large amount of labeled training images to obtain satisfactory accuracy, and manual labeling is labor expensive and time consuming. To address this issue, in this paper, we propose a novel semi-supervised deep learning framework, namely semi-supervised deep linear discriminant analysis, by taking advantage of the deep neural network (DNN) and the graph to simultaneously exploit the semantic information of labeled and unlabeled images for classification. Specifically, we first replace the loss function of the DNN with the objective function of linear discriminant analysis to produce features minimizing the intra-class distance yet maximizing the inter-class distance, in order to construct a robust and effective graph Laplacian. Afterwards, we design a new objective function via employing the graph constructed by features of labeled and un-labeled images, and then adopt this objective as the loss function of the DNN to produce features for classification. Experiments on skeletal muscle and lung cancer images demonstrate the proposed framework outperforms several recent state of the arts.