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Medical image analysis is motivated by the success of deep learning, where annotations are usually expensive and not easy to obtain. In this paper, we propose a deep quintuplet network CXNet-m3, where the classification of lesion type of chest x-ray images (CXRs) could benefit from easily accessible annotations like patient age, gender, identity and view position. To improve classification performance, a novel loss function combining both deep metric learning and deep learning is first designed based on multiple labels. Then, a deep model based on transfer learning is built to optimize the loss function. To solve the problem of slow convergence, a quintuplet mining algorithm is presented to provide valuable training samples for the proposed classification model. The experimental results on Chest X-ray14 database show that our classification method outperforms some state-of-art models under Area Under Curve (AUC) score, reaching 0.824 on an average. Besides, our proposal achieves more than 0.9 AUC values in the case of Infiltration, Atelectasis, Cardiomegaly and Nodule.

Cxnet-M3: A Deep Quintuplet Network for Multi-Lesion Classification in Chest X-Ray Images Via Multi-Label Supervision