Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology

Abstract

Purpose: Completely labeled datasets of pathology slides are often difficult and time consuming to obtain. Semi-supervised learning methods are able to learn reliable models from small number of labeled instances and large quantities of unlabeled data. In this paper, we explored the potential of clustering analysis for semi-supervised support vector machine (SVM) classifier. Method: A clustering analysis method was proposed to find regions of high density prior to finding the decision boundary using a supervised SVM and was compared with another state-of-the-art semi-supervised technique. Different percentages of labeled instances were used to train supervised and semi-supervised SVM learners from an image dataset generated from 50 whole-mount images (8 patients) of breast specimen. Their cross-validated classification performances were compared with each other using the area under the ROC curve measure. Result: Our proposed clustering analysis for semi-supervised learning was able to produce a reliable classification model from small amounts of labeled data. Comparing the proposed method in this study with a well-known implementation of semi-supervised SVM, our method performed much faster and produced better results.