Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis : A Survey

Abstract

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.