DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays

Author(s)

Teixeira, Vinicius ; Braz, Leodecio ; Pedrini, Helio ; Dias, Zanoni ; Paiva, AC ; Conci, A ; Braz, G ; Almeida, JDS ; Fernandes, LAF

• Document type: Journal article
• Source: PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 27TH EDITION; v. N/A, p. 6-pg., 2020-01-01.

Abstract

The chest radiography is one of the most accessible radiological examinations for diagnosis of lung and heart diseases. Deep learning techniques have been increasingly used to provide more accurate detection of thorax lesions on Chest X-Ray (CXR) images. However, we observe that we can use the complementarity of dual asymmetric deep convolutional neural networks (DCNNs) to improve the ability of CXR image classification compared to the single network. In this paper, we propose a novel dual lesion attention network named DuaLAnet for the classification of 14 thorax diseases on chest radiography. The DuaLAnet consists of two asymmetric attention networks, DenseNet-169 and ResNet-152, to integrate the advantages into a wider architecture, thus extracting more discriminative features of different abnormalities from the raw CXRs. Moreover, a training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss. The proposed DuaLAnet has been evaluated against eight deep learning models using the patient-wise official split of the ChestX-ray14 dataset [1]. Our results show that DuaLAnet achieves and average per-class AUC of 0.820 in the experiments, which clearly substantiate the effectiveness of DuaLAnet when compared to the state-of-the-art baselines. (AU)

• FAPESP's process: 17/16246-0 - Sensitive media analysis through deep learning architectures
• Grantee: Sandra Eliza Fontes de Avila
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/20875-8 - Chest X-ray image classification using deep neural networks
• Grantee: Vinicius Teixeira de Melo
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points
• Grantee: Flávio Keidi Miyazawa
• Support Opportunities: Research Projects - Thematic Grants