Institute of Medical Systems Biology, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
Sci Rep. 2024 Mar 8;14(1):5695. doi: 10.1038/s41598-024-56079-1.
The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in other domains. This is mainly due to the black box characteristic of most optimized models and the undetermined generalization ability of the trained architectures. The current work tackles both issues in the radiology domain by focusing on developing an effective and interpretable cardiomegaly detection architecture based on segmentation models. The architecture consists of two distinct neural networks performing the segmentation of both cardiac and thoracic areas of a radiograph. The respective segmentation outputs are subsequently used to estimate the cardiothoracic ratio, and the corresponding radiograph is classified as a case of cardiomegaly based on a given threshold. Due to the scarcity of pixel-level labeled chest radiographs, both segmentation models are optimized in a semi-supervised manner. This results in a significant reduction in the costs of manual annotation. The resulting segmentation outputs significantly improve the interpretability of the architecture's final classification results. The generalization ability of the architecture is assessed in a cross-domain setting. The assessment shows the effectiveness of the semi-supervised optimization of the segmentation models and the robustness of the ensuing classification architecture.
尽管人工智能在其他领域取得了重大成功，但神经网络在临床环境中的成功整合仍然很少见。这主要是由于大多数优化模型的黑盒特性和训练架构的不确定泛化能力。目前的工作通过专注于开发一种基于分割模型的有效且可解释的心胸比增大检测架构来解决放射学领域的这两个问题。该架构由两个截然不同的神经网络组成，用于分割 X 光片的心脏和胸部区域。随后，分别使用各自的分割输出来估计心胸比，并根据给定的阈值将相应的 X 光片分类为心胸比增大的病例。由于像素级标记的胸部 X 光片稀缺，因此以半监督的方式对两个分割模型进行了优化。这大大降低了手动标注的成本。分割输出显著提高了架构最终分类结果的可解释性。在跨域设置中评估架构的泛化能力。评估结果表明，分割模型的半监督优化和随之而来的分类架构的稳健性是有效的。
