  Deep neural networks, including Convolutional Neural Networks (CNNs) and
Visual Transformers (ViT), have achieved stunning success in medical image
domain. We study thorax disease classification in this paper. Effective
extraction of features for the disease areas is crucial for disease
classification on radiographic images. While various neural architectures and
training techniques, such as self-supervised learning with
contrastive/restorative learning, have been employed for disease classification
on radiographic images, there are no principled methods which can effectively
reduce the adverse effect of noise and background, or non-disease areas, on the
radiographic images for disease classification. To address this challenge, we
propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is
universally applicable to the training of all neural networks. The LRFL method
is both empirically motivated by the low frequency property observed on all the
medical datasets in this paper, and theoretically motivated by our sharp
generalization bound for neural networks with low-rank features. In the
empirical study, using a neural network such as a ViT or a CNN pre-trained on
unlabeled chest X-rays by Masked Autoencoders (MAE), our novel LRFL method is
applied on the pre-trained neural network and demonstrate better classification
results in terms of both multiclass area under the receiver operating curve
(mAUC) and classification accuracy.
