基于深度多尺度相似网络的肾上腺肿块CT图像亚类鉴别
Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images
摘要：
计算机断层扫描（CT）检测到的肾上腺肿块病变（肾上腺肿块）的准确分类对于诊断和患者管理非常重要。肾上腺肿块可以是良性或恶性的，良性肿块的患病率各不相同。基于卷积神经网络（CNN）的分类方法是最大化大型医学成像训练数据集中类间差异的最新技术。由于大的类内变化、大的类间相似性和由于肿块病变的大小而导致的不平衡的训练数据，将CNN应用于肾上腺肿块是具有挑战性的。我们开发了一个深度多尺度相似性网络（DMRN）来克服这些限制，并利用成对的CNN来评估类内相似性。我们使用多尺度特征嵌入来提高类间可分性，通过迭代组合在输入的不同尺度产生的互补信息来创建结构化特征描述符。我们用随机抽样的成对肾上腺肿块来增强训练数据，以减少不平衡的训练数据的影响。我们使用了229例肾上腺肿块患者的CT扫描进行评估。在五重交叉验证中，与最先进的方法相比，我们的方法具有最佳结果（准确率为89.52%）（p<0.05）。我们在ImageCLEF 2016竞赛数据集上对我们的方法进行了概括性分析，该数据集包括30个类别的6，776张图像的训练集和4，166张图像的测试集。我们的方法取得了更好的分类性能（85.90%的准确率）相比，现有的方法和竞争力相比，需要额外的训练数据的方法（1.47%的准确率降低）。我们的DMRN在CT上对肾上腺肿块进行了细分，优于最先进的方法。
英文摘要：
The accurate classification of mass lesions in the adrenal glands (adrenal masses), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses have varying prevalence. Classification methods based on convolutional neural networks (CNNs) are the state-of-the-art in maximizing inter-class differences in large medical imaging training datasets. The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions. We developed a deep multi-scale resemblance network (DMRN) to overcome these limitations and leveraged paired CNNs to evaluate the intra-class similarities. We used multi-scale feature embedding to improve the inter-class separability by iteratively combining complementary information produced at different scales of the input to create structured feature descriptors. We augmented the training data with randomly sampled paired adrenal masses to reduce the influence of imbalanced training data. We used 229 CT scans of patients with adrenal masses for evaluation. In a five-fold cross-validation, our method had the best results (89.52% in accuracy) when compared to the state-of-the-art methods (p<0.05). We conducted a generalizability analysis of our method on the ImageCLEF 2016 competition dataset for medical subfigure classification, which consists of a training set of 6,776 images and a test set of 4,166 images across 30 classes. Our method achieved better classification performance (85.90% in accuracy) when compared to the existing methods and was competitive when compared with methods that require additional training data (1.47% lower in accuracy). Our DMRN sub-classified adrenal masses on CT and was superior to state-of-the-art approaches.
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