  Computer-aided X-ray pneumonia lesion recognition is important for accurate
diagnosis of pneumonia. With the emergence of deep learning, the identification
accuracy of pneumonia has been greatly improved, but there are still some
challenges due to the fuzzy appearance of chest X-rays. In this paper, we
propose a deep learning framework named Attention-Based Contrastive Learning
for Class-Imbalanced X-Ray Pneumonia Lesion Recognition (denoted as Deep
Pneumonia). We adopt self-supervised contrastive learning strategy to pre-train
the model without using extra pneumonia data for fully mining the limited
available dataset. In order to leverage the location information of the lesion
area that the doctor has painstakingly marked, we propose mask-guided hard
attention strategy and feature learning with contrastive regulation strategy
which are applied on the attention map and the extracted features respectively
to guide the model to focus more attention on the lesion area where contains
more discriminative features for improving the recognition performance. In
addition, we adopt Class-Balanced Loss instead of traditional Cross-Entropy as
the loss function of classification to tackle the problem of serious class
imbalance between different classes of pneumonia in the dataset. The
experimental results show that our proposed framework can be used as a reliable
computer-aided pneumonia diagnosis system to assist doctors to better diagnose
pneumonia cases accurately.
