Building medical image classifiers with very limited data using segmentation networks

HighlightsBuilding medical image classifiers using features from segmentation networks.Important for 3D image analysis as ImageNet pre‐trained CNNs are unavailable in 3D.Tested on 3D brain tumor type and 2D cardiac semantic level classifications.Compared to classifiers trained from scratch and with ImageNet pre‐trained CNNs.82% and 86% accuracies on brain tumor and cardiac semantic level classifications. Graphical abstract Figure. No caption available. ABSTRACT Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre‐trained classification models can alleviate the problem, constrained image sizes and model complexities can lead to unnecessary increase in computational cost and decrease in performance. As many common morphological features are usually shared by different classification tasks of an organ, it is greatly beneficial if we can extract such features to improve classification with limited samples. Therefore, inspired by the idea of curriculum learning, we propose a strategy for building medical image classifiers using features from segmentation networks. By using a segmentation network pre‐trained on similar data as the classification task, the machine can first learn the simpler shape and structural concepts before tackling the actual classification problem which usually involves more complicated concepts. Using our proposed framework on a 3D three‐class brain tumor type classification problem, we achieved 82% accuracy on 191 testing samples with 91 training samples. When applying to a 2D nine‐class cardiac semantic level classification problem, we achieved 86% accuracy on 263 testing samples with 108 training samples. Comparisons with ImageNet pre‐trained classifiers and classifiers trained from scratch are presented.