  Automatic pulmonary nodules classification is significant for early diagnosis
of lung cancers. Recently, deep learning techniques have enabled remarkable
progress in this field. However, these deep models are typically of high
computational complexity and work in a black-box manner. To combat these
challenges, in this work, we aim to build an efficient and (partially)
explainable classification model. Specially, we use \emph{neural architecture
search} (NAS) to automatically search 3D network architectures with excellent
accuracy/speed trade-off. Besides, we use the convolutional block attention
module (CBAM) in the networks, which helps us understand the reasoning process.
During training, we use A-Softmax loss to learn angularly discriminative
representations. In the inference stage, we employ an ensemble of diverse
neural networks to improve the prediction accuracy and robustness. We conduct
extensive experiments on the LIDC-IDRI database. Compared with previous
state-of-the-art, our model shows highly comparable performance by using less
than 1/40 parameters. Besides, empirical study shows that the reasoning process
of learned networks is in conformity with physicians' diagnosis. Related code
and results have been released at: https://github.com/fei-hdu/NAS-Lung.
