  Computer-aided diagnosis plays a salient role in more accessible and accurate
cardiopulmonary diseases classification and localization on chest radiography.
Millions of people get affected and die due to these diseases without an
accurate and timely diagnosis. Recently proposed contrastive learning heavily
relies on data augmentation, especially positive data augmentation. However,
generating clinically-accurate data augmentations for medical images is
extremely difficult because the common data augmentation methods in computer
vision, such as sharp, blur, and crop operations, can severely alter the
clinical settings of medical images. In this paper, we proposed a novel and
simple data augmentation method based on patient metadata and supervised
knowledge to create clinically accurate positive and negative augmentations for
chest X-rays. We introduce an end-to-end framework, SCALP, which extends the
self-supervised contrastive approach to a supervised setting. Specifically,
SCALP pulls together chest X-rays from the same patient (positive keys) and
pushes apart chest X-rays from different patients (negative keys). In addition,
it uses ResNet-50 along with the triplet-attention mechanism to identify
cardiopulmonary diseases, and Grad-CAM++ to highlight the abnormal regions. Our
extensive experiments demonstrate that SCALP outperforms existing baselines
with significant margins in both classification and localization tasks.
Specifically, the average classification AUCs improve from 82.8% (SOTA using
DenseNet-121) to 83.9% (SCALP using ResNet-50), while the localization results
improve on average by 3.7% over different IoU thresholds.
