Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

TL;DR

This paper introduces a convolutional sparse kernel network (CSKN), an unsupervised learning framework designed for medical image analysis, capable of extracting meaningful features without labeled data, and performs comparably to supervised methods.

Contribution

The paper presents a novel hierarchical unsupervised feature learning framework that extends kernel learning, uses layer-wise pre-training, and incorporates multi-scale spatial pyramid pooling for medical images.

Findings

CSKN outperforms conventional unsupervised methods in accuracy.

CSKN achieves comparable accuracy to supervised CNNs.

Framework effectively leverages unannotated medical image data.

Abstract

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.