Unsupervised Deep Feature Learning for Medical Image Analysis

Abstract

The availability of annotated image datasets and recent advances in supervised deep learning methods are enabling the end-to-end derivation of representative image features that can impact a variety of image analysis problems. These supervised methods use prior knowledge derived from labelled training data and approaches, for example, convolutional neural networks (CNNs) have produced impressive results in natural (photographic) image classification. CNNs learn image features in a hierarchical fashion. Each deeper layer of the network learns a representation of the image data that is higher level and semantically more meaningful. However, the accuracy and robustness of image features with supervised CNNs are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are scarce mainly due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. The concept of ‘transfer learning’ – the adoption of image features from different domains, e.g., image features learned from natural photographic images – was introduced to address the lack of large amounts of labelled medical image data. These image features, however, are often generic and do not perform well in specific medical image analysis problems. An alternative approach was to optimise these features by retraining the generic features using a relatively small set of labelled medical images. This ‘fine-tuning’ approach, however, is not able to match the overall accuracy of learning image features directly from large collections of data that are specifically related to the problem at hand. An alternative approach is to use unsupervised feature learning algorithms to build features from unlabelled data, which then allows unannotated image archives to be used. Many unsupervised feature learning algorithms such as sparse coding (SC), auto-encoder (AE) and Restricted Boltzmann Machines (RBMs), however, have often been limited to learning low-level features such as lines and edges. In an attempt to address these limitations, in this thesis, we present several new unsupervised deep learning methods to learn semantic high-level features from unlabelled medical images to address the challenge of learning representative visual features in medical image analysis. We present two methods to derive non-linear and non-parametric models, which are crucial to unsupervised feature learning algorithms; one method embeds a kernel learning within CNNs while the other couples clustering with CNNs. We then further improved the quality of image features using domain adaptation methods (DAs) that learn representations that are invariant to domains with different data distributions. We present a deep unsupervised feature extractor to transform the feature maps from the pre-trained CNN on natural images to a set of non-redundant and relevant medical image features. Our feature extractor preserves meaningful generic features from the pre-trained domain and learns specific local features that are more representative of the medical image data. We conducted extensive experiments on 4 public datasets which have diverse visual characteristics of medical images including X-ray, dermoscopic and CT images. Our results show that our methods had better accuracy when compared to other conventional unsupervised methods and competitive accuracy to methods that used state-of-the-art supervised CNNs. Our findings suggest that our methods could scale to many different transfer learning or domain adaptation approaches where they have none or small sets of labelled data.