利用深度Transformer与可解释人工智能在心脏MRI模态中自动诊断心肌炎疾病
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
- The Graduate School of Biomedical Engineering, UNSW Sydney（新南威尔士大学悉尼分校生物医学工程研究生院）
- Data Science and Computational Intelligence Institute, University of Granada（格拉纳达大学数据科学与计算智能研究所）
- University of La Rioja（拉里奥哈大学）
- University of Technology Sydney (UTS)（悉尼科技大学）
- Macquarie University（麦考瑞大学）
- University of Leicester（莱斯特大学）
- Deakin University（迪肯大学）
- University of Cambridge（剑桥大学）
- University of Southern Queensland（南昆士兰大学）
- The University of New South Wales（新南威尔士大学）
- AI-enabled Processes (AIP) Research Centre, Macquarie University（麦考瑞大学人工智能赋能流程研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于深度Transformer和可解释AI的心脏MRI心肌炎自动诊断系统，在Z-Alizadeh数据集上TNT模型以10折交叉验证达到99.73%准确率，并用Grad Cam定位可疑区域。
AI中文摘要：
心肌炎是一种重要的心血管疾病，通过损害心肌而对许多个体的健康构成威胁。包括HIV在内的微生物和病毒的出现，在心肌炎疾病的发生中起着关键作用。心脏磁共振成像扫描过程中产生的图像对比度较低，这使得诊断心血管疾病具有挑战性。另一方面，对每位心血管疾病患者检查大量心脏磁共振成像切片对医生而言可能是一项困难的任务。为克服现有挑战，研究人员建议使用基于人工智能的计算机辅助诊断系统。本文介绍了一种利用深度学习方法从心脏磁共振图像中检测心肌炎疾病的计算机辅助诊断系统。所提出的计算机辅助诊断系统包括数据集、预处理、特征提取、分类和后处理等若干步骤。首先，选择Z-Alizadeh数据集进行实验。随后，心脏磁共振图像经过多种预处理步骤，包括去噪、尺寸调整，以及通过CutMix和MixUp技术进行的数据增强。接下来，使用最新的深度预训练模型和Transformer模型对心脏磁共振图像进行特征提取和分类。我们的研究结果表明，与预训练架构相比，Transformer模型在检测心肌炎疾病方面表现出更优越的性能。在深度学习架构方面，Turbulence Neural Transformer模型采用10折交叉验证方法，达到了99.73%的出色准确率。此外，为定位心脏磁共振成像图像中心肌炎疾病的可疑区域，采用了基于可解释性的Grad Cam方法。
英文摘要：
Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed.
