Learning Low-Rank Feature for Thorax Disease Classification

arXiv:2404.18933v1 Announce Type: cross
Abstract: 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 …

abstract architectures arxiv classification cnns convolutional convolutional neural networks cs.cv cs.lg disease domain extraction feature features image images low medical networks neural architectures neural networks paper study success transformers type visual vit while