使用混合CNN-Transformer架构的胸部X光片胸科疾病计算机辅助诊断
Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture
- Sonit Singh School of Computer Science
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出SA-DenseNet121，结合CNN与Transformer自注意力，在四个大型胸部X光数据集上实现多疾病诊断，提升AUC-ROC，支持放射科工作流程。
AI中文摘要：
医学影像已被用于诊断各种疾病，使其成为有效患者护理中最强大的资源之一。由于广泛可用性、低成本和低辐射，胸部X光片是诊断各种胸科疾病最常用的放射学检查之一。由于医学影像技术的进步和患者负荷的增加，当前的放射学工作流程面临各种挑战，包括积压增加、长时间工作以及诊断错误增多。一个能够解读胸部X光片以通过提供可操作见解来增强放射科医生的自动化计算机辅助诊断系统，有潜力为放射科医生提供第二意见，突出图像中的相关区域，从而加快临床工作流程、减少诊断错误并改善患者护理。在本研究中，我们应用了一种新颖的架构，通过使用Transformer的多头自注意力机制增强DenseNet121卷积神经网络（CNN），即SA-DenseNet121，能够识别胸部X光片中的多种胸科疾病。我们在四个最大的胸部X光片数据集上进行了实验，即ChestX-ray14、CheXpert、MIMIC-CXR-JPG和IU-CXR。以接收者操作特征曲线下面积（AUC-ROC）表示的实验结果表明，用自注意力增强CNN具有从胸部X光片诊断不同胸科疾病的潜力。所提出的方法有潜力支持阅读工作流程、提高效率并减少诊断错误。
英文摘要：
Medical imaging has been used for diagnosis of various conditions, making it one of the most powerful resources for effective patient care. Due to widespread availability, low cost, and low radiation, chest X-ray is one of the most sought after radiology examination for the diagnosis of various thoracic diseases. Due to advancements in medical imaging technologies and increasing patient load, current radiology workflow faces various challenges including increasing backlogs, working long hours, and increase in diagnostic errors. An automated computer-aided diagnosis system that can interpret chest X-rays to augment radiologists by providing actionable insights has potential to provide second opinion to radiologists, highlight relevant regions in the image, in turn expediting clinical workflow, reducing diagnostic errors, and improving patient care. In this study, we applied a novel architecture augmenting the DenseNet121 Convolutional Neural Network (CNN) with multi-head self-attention mechanism using transformer, namely SA-DenseNet121, that can identify multiple thoracic diseases in chest X-rays. We conducted experiments on four of the largest chest X-ray datasets, namely, ChestX-ray14, CheXpert, MIMIC-CXR-JPG, and IU-CXR. Experimental results in terms of area under the receiver operating characteristics (AUC-ROC) shows that augmenting CNN with self-attention has potential in diagnosing different thoracic diseases from chest X-rays. The proposed methodology has the potential to support the reading workflow, improve efficiency, and reduce diagnostic errors.
