  Convolutional Neural Networks (CNNs) intrinsically requires large-scale data
whereas Chest X-Ray (CXR) images tend to be data/annotation-scarce, leading to
over-fitting. Therefore, based on our development experience and related work,
this paper thoroughly introduces tricks to improve generalization in the CXR
diagnosis: how to (i) leverage additional data, (ii) augment/distillate data,
(iii) regularize training, and (iv) conduct efficient segmentation. As a
development example based on such optimization techniques, we also feature
LPIXEL's CNN-based CXR solution, EIRL Chest Nodule, which improved
radiologists/non-radiologists' nodule detection sensitivity by 0.100/0.131,
respectively, while maintaining specificity.
