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Discriminating the subtypes of non-small cell lung cancer (NSCLC) based on computed tomography (CT) images is a challenging task for radiologists. Although several machine learning methods such as radiomics, and deep learning methods such as convolutional neural networks (CNNs) have been proposed to explore the problem, large sample sizes are required for effective training, and this may not be easily achieved in single-center datasets.

quantitative imaging in medicine and surgery

Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics