ArticlePhysical and engineering sciences in medicine2024
A model fusion method based DAT-DenseNet for classification and diagnosis of aortic dissection.
Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Performance of image-based deep learning models for aortic dissection segmentation and diagnosis: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
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6 authors.
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Abstract
In this paper, we proposed a complete study method to achieve accurate aortic dissection diagnosis at the patient level. Based on the CT angiography (CTA) images, a classification model named DAT-DenseNet, which combined the deep attention Transformer module with the DenseNet architecture is proposed. In the first phase, two DAT-DenseNet are combined in parallel. It is used to accurately achieve two classification task at the CTA images. In the second stage, we propose a feature fusion module. It concatenates and fuses the image features output from the two classification models on a patient by patient basis. In the comparison experiments of classification model performance, DAT-DenseNet obtained 92.41
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