open access

https://hdl.handle.net/2115/98011

Pre-training Deep Neural Networks Using 3D Formula-Driven Supervised Learning for COPD Staging

英語

Medical informatics

2025-08-07

Springer

Artificial life and robotics

30

643-652

This study proposes a novel pre-training method employing the 3D formula-driven supervised learning for classifying three-dimensional computed tomography (CT) images, specifically for staging chronic obstructive pulmonary disease (COPD). The proposed method leverages mathematically generated 3D images as a pre-training dataset to enhance feature extraction while addressing the scarcity of labeled training data, primarily caused by ethical concerns inherent in medical imaging. This paper evaluates the effectiveness of the proposed method in two CT image classification tasks: COVID-19 classification and COPD staging. The experimental result shows that the performance of a COVID-19 classification model pre-trained using mathematically generated 3D images is competitive to the existing model pre-trained using numerous CT lung images. The result also reveals the potential of the proposed approach for COPD staging, mitigating the data scarcity issue.

学術雑誌論文

Accepted Manuscript

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10015-025-01051-z

1433-5298

1614-7456