Joined CNN and transformer network for thorax disease classification

Ruihua Zhang; Wenkai Chen; Wenjia Liu

doi:10.1117/12.3009247

16 October 2023 Joined CNN and transformer network for thorax disease classification

Ruihua Zhang, Wenkai Chen, Wenjia Liu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032R (2023) https://doi.org/10.1117/12.3009247
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Thorax disease classification is a complex problem due to lesions are scattered and different diseases are related. It will benefit the diagnosis to comprehensively analyze the context of the scattered lesions and multiple diseases. Most existing supervised methods use Convolutional Neural Networks (CNNs) to capture chest X-ray (CXR) features for classification, but usually ignore the context representation which is useful for multi-label classification tasks. In this paper, we propose a novel thorax disease classification network with joined CNN and Transformer (TC-CNNT) that learns global features and context representations simultaneously. Specifically, TC-CNNT includes a CNN branch to gradually extract global feature representations from low-level to high-level through convolutional filtering. Meanwhile, a transformer branch is designed to capture context-dependent features through the self-attention mechanism of the shift windows. In addition, the feature fusion and multi-loss strategy are applied to maximumly learn the complementary global and context representations. Finally, the TC-CNNT method is verified on the ChestX-ray14 dataset and compared with the state-of-the-art methods, the experimental results demonstrate its superior performance for thorax disease classification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruihua Zhang, Wenkai Chen, and Wenjia Liu "Joined CNN and transformer network for thorax disease classification", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032R (16 October 2023); https://doi.org/10.1117/12.3009247

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KEYWORDS

Diseases and disorders

Transformers

Chest imaging

Windows

Design and modelling

Neurological disorders

Pulmonary disorders

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