ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1593074
This article is part of the Research TopicApplication of Multimodal Data and Artificial Intelligence in Pulmonary DiseasesView all 5 articles
An Auxiliary Diagnostic Model Based on Joint Learning of Brain and Lung Data
Provisionally accepted
- 1Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, Beijing, China
- 2Tsinghua University, Beijing, Beijing, China
- 3School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
- 4Beihang University, Beijing, China
- 5Peng Cheng Laboratory, Shenzhen, Guangdong Province, China
- 6Hangzhou Innovation Institute, Beihang University, Hangzhou, Jiangsu Province, China
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Artificial intelligence has significantly improved diagnostic accuracy and efficiency in medical imaging-assisted diagnosis. However, existing systems often focus on a single disease, neglecting the pathological connections between diseases. To fully leverage multi-disease information, this paper proposes an auxiliary diagnostic model based on joint learning of brain and lung data(ADMBLD), aiming to enhance the comprehensiveness and accuracy of diagnoses through cross-disease correlation learning. The model integrates imaging data and clinical history of brain and lung diseases to identify potential correlations between different diseases, providing clinicians with more precise and comprehensive diagnostic support. Experimental results show that the model trained on both brain and lung data outperforms those trained separately, validating the effectiveness of the multi-disease joint learning diagnostic model.
Keywords: Multi-disease, Brain and Lung Data, Classification, segmentation, Datasets Augmentation
Received: 13 Mar 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Ye, Li, Hua and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yi Yang, Beihang University, Beijing, China
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