PKA2-Net: Prior Knowledge-Based Active Attention Network for Accurate Pneumonia Diagnosis on Chest X-Ray Images

To accurately diagnose pneumonia patients on a limited annotated chest X-ray image dataset, a prior knowledge-based active attention network (PKA 2 -Net 1 ) was constructed. The PKA 2 -Net uses improved ResNet as the backbone network and consists of residual blocks, novel subject enhancement and bac...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník 27; číslo 7; s. 3513 - 3524
Hlavní autoři: Fu, Yu, Xue, Peng, Zhang, Zhili, Dong, Enqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
active attention features
Adaptation models
candidate templates
Chest
Computer networks
Data models
Datasets
Deep learning
Energy distribution
Feature extraction
Feature maps
Generators
Medical diagnosis
Medical imaging
Pneumonia
Pneumonia diagnosis
prior knowledge
Pulmonary diseases
subject enhancement and background suppression block
Task analysis
X-ray imaging
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:To accurately diagnose pneumonia patients on a limited annotated chest X-ray image dataset, a prior knowledge-based active attention network (PKA 2 -Net 1 ) was constructed. The PKA 2 -Net uses improved ResNet as the backbone network and consists of residual blocks, novel subject enhancement and background suppression (SEBS) blocks and candidate template generators, where template generators are designed to generate candidate templates for characterizing the importance of different spatial locations in feature maps. The core of PKA 2 -Net is SEBS block, which is proposed based on the prior knowledge that highlighting distinctive features and suppressing irrelevant features can improve the recognition effect. The purpose of SEBS block is to generate active attention features without any high-level features and enhance the ability of the model to localize lung lesions. In SEBS block, first, a series of candidate templates T with different spatial energy distributions are generated and the controllability of the energy distribution in T enables active attention features to maintain the continuity and integrity of the feature space distributions. Second, Top- n templates are selected from T according to certain learning rules, which are then operated by a convolution layer for generating supervision information that can guide the inputs of SEBS block to form active attention features. We evaluated the PKA 2 -Net on the binary classification problem of identifying pneumonia and healthy controls on a dataset containing 5856 chest X-ray images (ChestXRay2017), the results showed that our method can achieve 97.63% accuracy and 0.9872 sensitivity.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3267057