# Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation
## 2 Related Work  
Deep learning, especially Convolutional Neural Network (CNN)-based CAD algorithms, has been extensively applied to histopathological image classification tasks. We categorize the related research works into three major classes. The first class focuses on lightweight feature extraction networks. The second class aims to extract more representative image features by modifying network structures. The third class proposes various semi-supervised and unsupervised domain adaptation methods to address the scarcity of data labels. For the lightweight feature extraction networks, [PERSON] et al. [6] proposed a IRRCNN model for breast histopathological image classification. By combining the advantages of an Inception Network, Residual Network, and Recurrent Convolutional Neural Network, the model achieved robust learning performance. [PERSON] et al. [7] introduced a deep learning framework called MuDeRN for breast histopathological image classification. This model integrates multi-layer feature information of a deep residual network, which helps the model capture more contextual information of histopathological images. Despite the powerful image feature extraction capability of CNNs, these large networks require large amounts of data and high computing power to support their performance. Therefore, improved lightweight CNNs have been proposed for histopathological image classification [8; 9]. [PERSON] et al. [10] proposed a novel Deep Second-order Pooling Network (DSoPN) for breast cancer histopathological image classification, which improves the performance by introducing second-order statistical features. [PERSON] et al. [11] presented a lightweight CNN model named MobiHisNet, aiming to reduce the time required for histopathological diagnosis through mobile edge computing. Apart from methods based on network structures, numerous domain adaptation algorithms have been applied to histopathological image classification [12; 13; 14]. Semi-supervised and unsupervised domain adaptations are more suitable for the CAD, and have attracted more attention from researchers. Since unsupervised DA methods lack the guidance of target domain labels, their experimental performance needs to be further improved compared with that of semi-supervised DA methods with a few labeled target samples [15; 16]. [PERSON] et al. [17] studied a multiple weighted semi-supervised DA model for whole slide image classification. [PERSON] et al. [18] used a few-shot approach to reduce the need for labeled data for lung and breast histopathological image classification. [PERSON] et al. [19] proposed a covariance matching method for semi-supervised domain adaptation, which combines multiple kernel versions and shows significant performance in both isomorphic and heterogeneous case experiments. Reconstructed domain adaptation can improve performance by reconstructing samples or features, which focus on common representations of features between domains and independent representations within a single domain [20]. The reconstruction of source and target domain samples or features is used as a secondary task. Different classes of auto-encoders are applied to learn the shared encoded representation between domains [21; 22; 23]. Moreover, Generative Adversarial Networks (GANs) are used to obtain circular maps, comparing the differences between the reconstructed image and the original image within the domain as the reconstruction loss [24; 25; 26]. GANs possess strong generative capabilities, but require specialized designs to effectively perform reconstruction tasks. While auto-encoders are particularly suited for reconstruction tasks as they directly restore input data by minimizing reconstruction errors [27], self-supervised learning methods [28] primarily focus on learning feature representations and typically do not directly optimize for data reconstruction performance.