Fetal alcohol syndrome (FAS) caused by prenatal alcohol exposure can result in a series of cranio-facial anomalies, and behavioral and neurocognitive problems. Current diagnosis of FAS is typically done by identifying a set of facial characteristics, which are often obtained by manual examination. Anatomical landmark detection, which provides rich geometric information, is important to detect the presence of FAS associated facial anomalies. This imaging application is characterized by large variations in data appearance and limited availability of labeled data. Current deep learning-based heatmap regression methods designed for facial landmark detection in natural images assume availability of large datasets and are therefore not well-suited for this application. To address this restriction, we develop a new regularized transfer learning approach that exploits the knowledge of a network learned on large facial recognition datasets. In contrast to standard transfer learning which focuses on adjusting the pre-trained weights, the proposed learning approach regularizes the model behavior. It explicitly reuses the rich visual semantics of a domain-similar source model on the target task data as an additional supervisory signal for regularizing landmark detection optimization. Specifically, we develop four regularization constraints for the proposed transfer learning, including constraining the feature outputs from classification and intermediate layers, as well as matching activation attention maps in both spatial and channel levels. Experimental evaluation on a collected clinical imaging dataset demonstrate that the proposed approach can effectively improve model generalizability under limited training samples, and is advantageous to other approaches in the literature.

中文翻译：

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使用正则化迁移学习进行面部解剖标志检测并应用于胎儿酒精综合症识别


产前酒精暴露引起的胎儿酒精综合症（FAS）可导致一系列颅面部异常以及行为和神经认知问题。目前 FAS 的诊断通常是通过识别一组面部特征来完成，这些特征通常是通过手动检查获得的。解剖标志检测可提供丰富的几何信息，对于检测 FAS 相关面部异常的存在非常重要。该成像应用的特点是数据外观变化较大且标记数据的可用性有限。当前为自然图像中的面部特征点检测而设计的基于深度学习的热图回归方法假设有大型数据集的可用性，因此不太适合此应用。为了解决这一限制，我们开发了一种新的正则化迁移学习方法，该方法利用在大型面部识别数据集上学习的网络知识。与侧重于调整预训练权重的标准迁移学习相比，所提出的学习方法规范了模型行为。它明确地重用目标任务数据上的域相似源模型的丰富视觉语义作为用于规范地标检测优化的附加监督信号。具体来说，我们为所提出的迁移学习开发了四个正则化约束，包括约束分类层和中间层的特征输出，以及在空间和通道级别上匹配激活注意图。对收集的临床影像数据集的实验评估表明，所提出的方法可以有效提高有限训练样本下的模型泛化性，并且优于文献中的其他方法。