Gwak Seunghyun, Yang Sooyoung, Jeong Heawon, Park Junhu, Kang Myungjoo
Computational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.
Biomed Eng Lett. 2025 May 22;15(4):785-795. doi: 10.1007/s13534-025-00478-4. eCollection 2025 Jul.
This study proposes a deep learning-based diagnostic model called the Projection-wise Masked Autoencoder (ProMAE) for rapid and accurate COVID-19 diagnosis using sparse-view CT images. ProMAE employs a column-wise masking strategy during pre-training to effectively learn critical diagnostic features from sinograms, even under extremely sparse conditions. The trained ProMAE can directly classify sparse-view sinograms without requiring CT image reconstruction. Experiments on sparse-view data with 50%, 75%, 85%, 95%, and 99% sparsity show that ProMAE achieves a diagnostic accuracy of over 95% at all sparsity levels and, in particular, outperforms ResNet, ConvNeXt, and conventional MAE models in COVID-19 diagnosis in environments with 85% or higher sparsity. This capability is especially advantageous for the development of portable and flexible imaging systems during large-scale outbreaks such as COVID-19, as it ensures accurate diagnosis while minimizing radiation exposure, making it a vital tool in resource-limited and high-demand settings.
本研究提出了一种基于深度学习的诊断模型，称为逐投影掩码自动编码器（ProMAE），用于使用稀疏视图CT图像快速准确地诊断新冠肺炎。ProMAE在预训练期间采用逐列掩码策略，即使在极其稀疏的条件下，也能有效地从正弦图中学习关键诊断特征。经过训练的ProMAE可以直接对稀疏视图正弦图进行分类，而无需进行CT图像重建。在稀疏度为50%、75%、85%、95%和99%的稀疏视图数据上进行的实验表明，ProMAE在所有稀疏度水平上均实现了超过95%的诊断准确率，特别是在稀疏度为85%或更高的环境中，在新冠肺炎诊断方面优于ResNet、ConvNeXt和传统MAE模型。这种能力对于在新冠肺炎等大规模疫情期间开发便携式和灵活的成像系统尤为有利，因为它在确保准确诊断的同时将辐射暴露降至最低，使其成为资源有限和需求高的环境中的重要工具。
