Deep learning-based methods have shown promising results in multi-label chest X-ray (CXR) image classification. However, most existing methods rely on large-scale fully-annotated datasets, which are costly and laborious to obtain. Therefore, training a high-performance model with limited annotation remains a significant challenge in practice. To address this issue, we propose an Adversarial and Correlation-Aware Data Augmentation (ACAA) framework for multi-label CXR image classification. First, we generate pseudo labels based on the model predictions on weakly-augmented images. Next, we propose a Generalized Nesterov Iterative Fast Gradient Sign Method (GNI-FGSM) to generate effective adversarial examples as image-level strongly-augmented data. Then, we introduce an image-level adversarial-augmentation-based consistency regularization to perform supervision of model predictions on the above image-level adversarial examples. To explore more diverse data transformations, we further perform adversarial augmentation in the feature space by imposing perturbations on the extracted features of each image and introduce a feature-level adversarialaugmentation- based consistency regularization. Furthermore, we propose a Batch-level Mamba module (Batch- Mamba) coupled with a batch-level Mamba-based correlation regularization to explore inter-sample correlations along batch dimension. Extensive experiments on two large CXR datasets (CheXpert and MIMIC-CXR) demonstrate the effectiveness of the proposed ACAA framework for multilabel CXR image classification under limited-annotation scenario.
Liang et al. (Thu,) studied this question.