论文标题
深层监督的转移网络，用于诊断乳腺癌的超声检查方式不平衡
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
论文作者
论文摘要
弹性超声（EUS）在诊断乳腺癌的诊断中提供了有关B模式超声（BUS）病变的其他生物力学内形式。但是，由于乡村医院缺乏EUS设备，因此对公共汽车和EUS的联合利用并不流行，这引起了乳腺癌计算机辅助诊断（CAD）中新型的模式不平衡问题。当前的转移学习（TL）几乎不关注本临床方式不平衡的特刊，即，源域（EUS模式）的标签样品比目标域中的样本少（总线模式）。此外，这些TL方法无法完全使用标签信息来探索两种模式之间的内在关系，然后指导促进的知识转移。为此，我们提出了一个新颖的双重监督TL网络（DDSTN），该网络（DDSTN）使用特权信息（LUPI）范式集成了学习，并将最大平均差异（MMD）标准纳入统一的深层TL框架。所提出的算法不仅可以充分利用共享标签来有效地指导Lupi范式的知识转移，而且还可以在未配对的数据之间执行其他超固定的转移。我们进一步介绍了MMD标准以增强知识转移。乳房超声数据集的实验结果表明，所提出的DDSTN优于与基于BUS的CAD的所有最新算法相比。
Elastography ultrasound (EUS) provides additional bio-mechanical in-formation about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality im-balance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreover, these TL methods cannot fully use the label information to explore the intrinsic relation between two modalities and then guide the promoted knowledge transfer. To this end, we propose a novel doubly supervised TL network (DDSTN) that integrates the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean Discrepancy (MMD) criterion into a unified deep TL framework. The proposed algorithm can not only make full use of the shared labels to effectively guide knowledge transfer by LUPI paradigm, but also perform additional super-vised transfer between unpaired data. We further introduce the MMD criterion to enhance the knowledge transfer. The experimental results on the breast ultra-sound dataset indicate that the proposed DDSTN outperforms all the compared state-of-the-art algorithms for the BUS-based CAD.
