## 2 Related Work
### Classification  
The most active area of research is image classification, which is used in disease classification and cancer diagnosis. In particular, various deep learning methods have been proposed for cervical cancer analysis. The study of [12] used the pyramid histogram of local binary patterns (PLBP), pyramid histogram in LAB color space (PLAB), and pyramid histogram of oriented gradients (PHOG) to extract features and classified the extracted features using various machine learning methods, such as random forest, AdaBoost, support vector machine (SVM), and logistic regression. In addition, cervical cancer was classified through an RoI pretreatment method and a pre-trained CaffeNet model. In [13], the authorsproposed the LeNet model, which combined additional preprocessing and image size changes using 345 positive and 345 negative images randomly extracted from the NCI data. However, there was a bias because the training set and the validation set contained the same session images. The study of [14] introduced the Faster R-CNN model [15] to detect cervical region and then classified the types of cancer with the extracted region. In [16], the authors used two pre-trained models for cervix detection and cancer classification. The authors trained the models to detect only the cervix area from Intel and MobileODT images using the YOLO (you only look once) algorithm and extracted the RoIs from the U.S. National Cancer Institute (NCI) images through the trained model. Cervical cancer was classified through the CNN-based GoogLeNet model from the extracted RoIs at a high level of performance. Most previous studies used labeled data to extract visual features, which they then employed to classify cancer. Because the amount of labeled data is limited due to the nature of medical images, we tried to improve the model's performance through RoI extraction. The study of [17] presented a CNN model, but the authors applied patch weight learning and a spatial regulator with Gaussian distribution to detect only cervical region. Ref. [18] proposed an ensembled transfer learning framework for classifying differentiation stage of cervical histopathology images. Ref. [19] proposed variant CNN models with a transfer learning approach to diagnose the stage of cervical cancer by classifying the cervix images using the ODT Kaggle dataset. Also, ref. [20] proposed a cervical cell image generation model based on taming transformers [21] to provide high-quality cervical cancer datasets and used Tokens-to-Token Vision Transformers (T2T-ViTs) [22] combing transfer learning to classify the cervical cancer. As for recent works, refs. [23; 24] used variant CNN models and adopted transfer learning to fine-tune models. Ref. [25] introduced a CerviFormer that depends on transformers and used a cross-attention module which enables it to manage very large-scale inputs. Furthermore, there have been other studies with various applications, including colonoscopy frame classification, polyp detection, pulmonary embolism detection, diabetic macular edema classification, and pneumonia detection. Refs. [26; 27] introduced an active incremental fine-tuning method and layer-wise fine-tuning method, respectively, using a pre-trained AlexNet model. The authors demonstrated their method on a small dataset with colonoscopy videos and computed tomography (CT) pulmonary angiography. Ref. [28] experimented with the Inception V3 model pre-trained on the ImageNet dataset on the OCT dataset and chest X-ray images. Refs. [26; 27; 28] showed that fine-tuned deep models are more robust than models from scratch in various medical imaging tasks. Moreover, ref. [29] introduced a classification task of malignant and benign breast masses and used a deep CNN model with stochastic gradient descent on small mammography images. The authors demonstrated that multi-task transfer learning can be an effective approach for training a deep CNN when training samples are limited. Ref.