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• W3092743670 startingPage "101846" @default.
• W3092743670 abstract "• Propose triple attention n etwork (A 3 Net) for thoracic disease diagnosis on chest X ray. • Learn channel wise, element wise, and scale wise attention s imultaneously. • Incorporate three attention learning mechanisms in to a deep classification model. • A chiev e highest average AUC on the ChestX ray14 dataset without using external data. Chest X-ray is the most common radiology examinations for the diagnosis of thoracic diseases. However, due to the complexity of pathological abnormalities and lack of detailed annotation of those abnormalities, computer-aided diagnosis (CAD) of thoracic diseases remains challenging. In this paper, we propose the triple-attention learning (A 3 Net) model for this CAD task. This model uses the pre-trained DenseNet-121 as the backbone network for feature extraction, and integrates three attention modules in a unified framework for channel-wise, element-wise, and scale-wise attention learning. Specifically, the channel-wise attention prompts the deep model to emphasize the discriminative channels of feature maps; the element-wise attention enables the deep model to focus on the regions of pathological abnormalities; the scale-wise attention facilitates the deep model to recalibrate the feature maps at different scales. The proposed model has been evaluated on 112,120images in the ChestX-ray14 dataset with the official patient-level data split. Compared to state-of-the-art deep learning models, our model achieves the highest per-class AUC in classifying 13 out of 14 thoracic diseases and the highest average per-class AUC of 0.826 over 14 thoracic diseases." @default.
• W3092743670 created "2020-10-22" @default.
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• W3092743670 date "2021-01-01" @default.
• W3092743670 modified "2023-10-12" @default.
• W3092743670 title "Triple attention learning for classification of 14 thoracic diseases using chest radiography" @default.
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• W3092743670 doi "https://doi.org/10.1016/j.media.2020.101846" @default.
• W3092743670 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33129145" @default.
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