## 1 Introduction
A novel hybrid transfer learning technique was proposed to detect cervical cancer from cervix images [33]. A hierarchical modular neural network architecture, was reported for automated screening of cervical cancer [34]. These studies have greatly advanced the process in the field of cervical cells classification. Despite of the recent significant progress in cervical cells classification, the high-precision accuracy remains a major obstacle for the fine-grained classification task. Most studies focus on the 2-class classification problem (normal or abnormal). A broad classification is useful for screening. Nonetheless, it is not enough for guiding clinical decisions on the diagnoses and treatments. Selection of treatment method depends on the phase of disease development. Fine-grained classification of cervical cells is of guiding significance to differentiate different disease phases and respective causes. Therefore, an appropriate treatment can be guaranteed. However, the CNN based approach achieved a highest accuracy of 98.3% [4] for the 2-class task on the Herlev dataset, while it only reached an accuracy of 64.1% for the 7-class task [32]. Thus, this indicates the need to find improved and efficient ways to classify cervical cells to finer categories automatically. In our study, a transfer learning [35] based snapshot ensemble [36] method (TLSE) is proposed for the fine-grained cervical cells classification task. The snapshot ensemble aims at acquiring an ensemble result without a multi-step process, while the transfer learning provides a solution for the small sample cervical cells dataset with pre-trained model of good generalization. A new training strategy is conducted for the unification of the integration. The original snapshot ensemble method provides an ensemble benefit within a single model training process instead of a traditional multi-step process, which lowers the computational cost. This method has been used in some application areas, such as aerial scene classification [37] and fault diagnosis [38], but has not been tested or used for cervical cells classification. Furthermore, a disadvantage of snapshot ensemble is that the CNN model needs to be trained from scratch, which means that adequate amounts of raw samples are needed to ensure the training procedure. As for the cervical cells classification task, well-labeled data are limited because collecting high quality labeled data is very time consuming and costly. The small sample data may not provide enough information for training the whole model from scratch. On the other hand, transfer learning provides a solution to address this issue, which helps to eliminate the need of training the whole network all over again. The transfer learning method is widely adopted in several medical application areas, such as breast cancer diagnosis [39], epithelium-stroma classification [40], recognition of metastatic tissue in lymph node sections [41], lung cancer recognition [42], and cervical cells classification [4, 32]. The proposed TLSE is meaningful to ensure the training of snapshot ensemble without the constraint of training the network with sufficient data from scratch. Furthermore, the advantages of the deep CNN model can also be leveraged into the cervical cells classification task, as the transfer learning phase makes the training easier. The rest of the paper is organized as follows: a detailed description of the proposed transfer learning based snapshot ensemble (TLSE) is illustrated in Section 2. The experiment results conducted to evaluate the proposed method are reported in Section 3. The discussions and future work are presented in Section 4. The conclusion are summarized in Section 5.