School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
CSIRO Data61, Sydney, Australia.
Sci Rep. 2024 Nov 22;14(1):28983. doi: 10.1038/s41598-024-79494-w.
Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique.
使用深度学习分析胸部 X 光片（CXR）中的肺部疾病存在重大挑战，这是由于疾病进展和不同 X 射线设置导致的肺部外观广泛变化所致。虽然深度学习模型在对具有正常或轻度异常发现的 CXR 图像进行肺部分割方面取得了显著的成功，但当面对复杂结构（如肺部混浊）时，其性能会下降。在这项研究中，我们提出了 AMRU++，这是一种基于注意力的多残差 UNet++网络，旨在对具有正常和严重异常的 CXR 图像进行稳健和准确的肺部分割。该模型采用了注意力模块来捕获相关的空间信息，以及多残差块来提取肺部区域丰富的上下文和有区分性的特征。为了进一步提高分割性能，我们引入了一种数据增强技术，模拟 CXR 病理的特征和特征，解决了注释数据有限的问题。我们在包含 350 例尘肺、COVID-19 和肺结核的公共和私人数据集上进行了广泛的实验，验证了我们提出的框架和数据增强技术的有效性。
