ArticleComputational and mathematical methods in medicine2022
Multisemantic Level Patch Merger Vision Transformer for Diagnosis of Pneumonia.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.Sensors (Basel, Switzerland) · 2025Article
- Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.Journal of clinical medicine · 2024Article
- Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
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2 authors.
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Abstract
The most popular test for pneumonia, a serious health threat to children, is chest X-ray imaging. However, the diagnosis of pneumonia relies on the expertise of experienced radiologists, and the scarcity of medical resources has forced us to conduct research on CAD (computer-aided diagnosis). In this study, we propose MP-ViT, the Multisemantic Level Patch Merger Vision Transformer, to achieve automatic diagnosis of pneumonia in chest X-ray images. We introduce Patch Merger to reduce the computational cost of ViT. Meanwhile, the intermediate results calculated by Patch Merger participate in the final classification in a concise way, so as to make full use of the intermediate information of the high-level semantic space to learn from local to overall and to avoid information loss caused by Patch Merger. We conducted experiments on a dataset with 3,883 chest X-ray images described as pneumonia and 1,349 images labeled as normal, and the results show that even without pretraining ViT on a large dataset, our model can achieve the accuracy of 0.91, the precision of 0.92, the recall of 0.89, and the
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