Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be selected for ensemble. On the other hand, due to the small training samples, the advantages of powerful CNN models may not be effectively leveraged. In order to address such a challenging issue, this paper proposes a transfer learning based snapshot ensemble (TLSE) method by integrating snapshot ensemble learning with transfer learning in a unified and coordinated way. Snapshot ensemble provides ensemble benefits within a single model training procedure, while transfer learning focuses on the small sample problem in cervical cells classification. Furthermore, a new training strategy is proposed for guaranteeing the combination. The TLSE method is evaluated on a pap-smear dataset called Herlev dataset and is proved to have some superiorities over the exiting methods. It demonstrates that TLSE can improve the accuracy in an ensemble manner with only one single training process for the small sample in fine-grained cervical cells classification.

中文翻译：

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使用基于迁移学习的快照集成改进计算机辅助宫颈细胞分类

宫颈细胞分类是计算机辅助宫颈癌检测的重要组成部分。在指导诊断和治疗的临床决策时，细粒度分类具有重要的临床意义，这仍然非常具有挑战性。最近，卷积神经网络 (CNN) 提供了一种通过使用自动学习的特征对宫颈细胞进行分类的新方法。尽管 CNN 模型的集成可以增加模型多样性并潜在地提高分类精度，但这是一个多步骤的过程，因为需要分别训练多个 CNN 模型，然后选择集成。另一方面，由于训练样本较少，可能无法有效利用强大的 CNN 模型的优势。为了解决如此具有挑战性的问题，本文通过以统一协调的方式将快照集成学习与迁移学习相结合，提出了一种基于迁移学习的快照集成（TLSE）方法。快照集成在单个模型训练过程中提供集成优势，而迁移学习侧重于宫颈细胞分类中的小样本问题。此外，提出了一种新的训练策略来保证组合。TLSE 方法在称为 Herlev 数据集的巴氏涂片数据集上进行评估，并被证明比现有方法具有一些优势。这表明TLSE可以以集成方式提高细粒度宫颈细胞分类中小样本的单一训练过程的准确性。