Improving Thorax Disease Classification by Learning Low-Rank Features

The authors propose a novel Low-Rank Feature Learning (LRFL) method to effectively reduce the adverse effect of noise and background on radiographic images for thorax disease classification. LRFL promotes low-rank features by adding a truncated nuclear norm regularization term to the training loss, which helps discard high-frequency features related to noise and background.