The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPI-based CAD framework for breast cancer using privileged information in this work. The work offers both a classifier- or feature-level LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifier-level algorithms and six feature-level LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifier-level and deep learning-based feature-level LUPI algorithms can enhance the performance of a single-modal imaging-based CAD for breast cancer by relocating PI.

中文翻译：

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转移学习特权信息助力乳腺癌的CAD诊断

近年来，基于影像的计算机辅助诊断（CAD）可以提高乳腺癌的治疗效率。实际上，基于单一模态的方法在多模态CAD成像背后始终缺乏。但是，由于成像设备的限制，特别是在农村医院，单模态成像成为临床实践中诊断的最爱。当今流行的一种新的学习模型，即使用特权信息学习（LUPI），采用了额外的特权信息（PI）方式在培训阶段提供帮助，但是PI在测试阶段没有帮助。同时，PI和训练样本之间存在联系。然后将相同的内容重新分配给学习的模型。我们在这项工作中使用特权信息提出了一个基于LUPI的乳腺癌CAD框架。这项工作提供了分类器级别或特征级别的LUPI，其中信息从附加的PI模式转换为诊断模式。在六个分类器级算法和六个特征级LUPI算法之间进行了彻底的比较。在获得的两个主要数据集上的实验结果表明，所有分类器级和基于深度学习的特征级LUPI算法都可以通过重定位PI来增强基于单模态成像的乳腺癌CAD的性能。