Unsupervised Deep Transfer Feature Learning for Medical Image Classification

TL;DR

This paper introduces an unsupervised hierarchical feature learning method for medical image classification that reduces dependence on annotated data and outperforms transfer learning approaches.

Contribution

A novel hierarchical unsupervised feature extractor using a convolutional auto-encoder on top of pre-trained CNNs for medical images.

Findings

Higher classification accuracy than transfer-learned methods

Competitive with state-of-the-art supervised methods

Reduces need for annotated training data

Abstract

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a paucity of annotated data available due to the complexity of manual annotation. To overcome this problem, a popular approach is to use transferable knowledge across different domains by: 1) using a generic feature extractor that has been pre-trained on large-scale general images (i.e., transfer-learned) but which not suited to capture characteristics from medical images; or 2) fine-tuning generic knowledge with a relatively smaller number of annotated images. Our aim is to reduce the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed atop of a pre-trained convolutional neural network. Our approach constrains the rich and generic image features from the pre-trained domain to a sophisticated representation of the local image characteristics from the unannotated medical image domain. Our approach has a higher classification accuracy than transfer-learned approaches and is competitive with state-of-the-art supervised fine-tuned methods.