Digital image-based signatures of breast tumors may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond traditional human-engineered computer vision methods, tumor classification methods using transfer learning from deep convolutional neural networks (CNNs) are actively under development. This article will first discuss our progress in using CNN-based transfer learning to characterize breast tumors for various diagnostic, prognostic, or predictive image-based tasks across multiple imaging modalities, including mammography, digital breast tomosynthesis, ultrasound (US), and magnetic resonance imaging (MRI), compared to both human-engineered feature-based radiomics and fusion classifiers created through combination of such features. Second, a new study is presented that reports on a comprehensive comparison of the classification performances of features derived from human-engineered radiomic features, CNN transfer learning, and fusion classifiers for breast lesions imaged with MRI. These studies demonstrate the utility of transfer learning for computer-aided diagnosis and highlight the synergistic improvement in classification performance using fusion classifiers.

中文翻译：

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使用人体工程放射组学、深度卷积神经网络的迁移学习和融合方法比较乳腺 MRI 肿瘤分类

基于数字图像的乳腺肿瘤特征可能最终有助于设计特定于患者的乳腺癌诊断和治疗方法。除了传统的人类工程计算机视觉方法，使用来自深度卷积神经网络 (CNN) 的迁移学习的肿瘤分类方法正在积极开发中。本文将首先讨论我们在使用基于 CNN 的转移学习来表征乳腺肿瘤的进展，用于跨多种成像模式的各种诊断、预后或基于图像的预测任务，包括乳房 X 光检查、数字乳房断层合成、超声（美国）和磁共振成像（MRI），与通过这些特征的组合创建的基于人类工程特征的放射组学和融合分类器相比。第二，提出了一项新研究，报告综合比较了来自人类工程放射组学特征、CNN 迁移学习和融合分类器的特征的分类性能，这些特征用于 MRI 成像的乳腺病变。这些研究证明了迁移学习在计算机辅助诊断中的实用性，并突出了使用融合分类器在分类性能方面的协同改进。