Multi-label Thoracic Disease Image Classification with Cross-Attention Networks

• URL: http://arxiv.org/abs/2007.10859v1
• Date: Tue, 21 Jul 2020 14:37:00 GMT
• Title: Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
• Authors: Congbo Ma, Hu Wang, Steven C.H. Hoi
• Abstract summary: We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
• Score: 65.37531731899837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology image classification task is significantly more challenging as it is far more expensive to collect the training data where the labeled data is in nature multi-label; and more seriously samples from easy classes often dominate; training data is highly class-imbalanced problem exists in practice as well. To overcome these challenges, in this paper, we propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images, which can effectively excavate more meaningful representation from data to boost the performance through cross-attention by only image-level annotations. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class. The proposed method achieves state-of-the-art results.

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