Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
Department of Otolaryngology-Head and Neck Surgery, Kobe University Graduate School of Medicine, Kobe, Japan.
Sci Rep. 2020 Nov 9;10(1):19388. doi: 10.1038/s41598-020-76389-4.
We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amount of imbalanced data when anomaly detection (AD) was combined with deep leaning (DL) model and the L-constrained softmax loss. The purpose of this study was to evaluate whether the proposed method was more accurate than other commonly used DL or AD methods. Magnetic resonance (MR) images of 245 parotid tumors (22.5% malignant) were retrospectively collected. We evaluated the diagnostic accuracy of the proposed method (VGG16-based DL and AD) and that of classification models using conventional DL and AD methods. A radiologist also evaluated the MR images. ROC and precision-recall (PR) analyses were performed, and the area under the curve (AUC) was calculated. In terms of diagnostic performance, the VGG16-based model with the L-constrained softmax loss and AD (local outlier factor) outperformed conventional DL and AD methods and a radiologist (ROC-AUC = 0.86 and PR-ROC = 0.77). The proposed method could discriminate between benign and malignant parotid tumors in MR images even when only a small amount of data with imbalanced distribution is available.
我们假设,在鉴别腮腺良恶性肿瘤时,当异常检测(AD)与深度学习(DL)模型和 L 约束软最大值损失相结合时,少量不平衡数据也可以获得较高的诊断准确性。本研究旨在评估所提出的方法是否比其他常用的 DL 或 AD 方法更准确。回顾性收集了 245 例腮腺肿瘤(22.5%恶性)的磁共振(MR)图像。我们评估了基于 VGG16 的 DL 和 AD (基于局部异常因子的 AD)与传统 DL 和 AD 方法的分类模型的诊断准确性。一位放射科医生还评估了 MR 图像。进行了 ROC 和精度-召回率(PR)分析,并计算了曲线下面积(AUC)。在诊断性能方面,基于 L 约束软最大值损失和 AD(局部异常因子)的 VGG16 模型优于传统的 DL 和 AD 方法以及放射科医生(ROC-AUC=0.86,PR-ROC=0.77)。该方法即使在数据分布不平衡且数量较少的情况下,也可在 MR 图像中区分腮腺良恶性肿瘤。