# Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation
## 1 Introduction  
Breast cancer is one of the most serious diseases threatening women's health. Accurate diagnosis is crucial for patient survival. Among various diagnostic methods, clinical histopathological diagnosis serves as the gold standard for breast cancer detection. However, manual detection is not only time-consuming and labor-intensive, but also requires the experience of examiners. As an effective computer-aided diagnostic (CAD) tool, deep learning has been extensively applied to breast histopathological image classification tasks [1, 2, 3]. The key to address the contradiction between high detection demand and limited examiners lies in establishing efficient deep learning models.  
Developing stable deep learning diagnostic algorithms faces two challenges. Firstly, deep models require a large amount of labeled data; otherwise, they may suffer from insufficient generalization ability and overfitting [4, 5]. Labeling histopathological images requires an experienced pathologist, and the labeling process is extremely time-consuming. Developing deep models based on limited labeled data poses a significant challenge. Additionally, low-level features such as texture and shape are very useful for histopathological image classification. Hence, efficiently fusing multi-level features represents another challenge.  
Our motivation is to address the two challenges faced by histopathological image classification models. A semi-supervised domain adaptation (DA) method is proposed to address the labelled data shortage issue, and on the other hand, an auto-encoder is applied to reconstruct image features, refining and preserving useful information for classification. We aim to enhance the performance of domain adaptation algorithms by reconstructing sample features.  
This paper introduces an auto-encoder reconstructed semi-supervised domain adaptation algorithm for breast histopathological image classification. This algorithm employs a deep transfer network to obtain multi-level features of histopathological images. The samples from two domains are confused using similarity loss and domain discriminators in order to blur the boundaries between domains. A lightweight auto-encoder is created for feature reconstruction. The encoder not only compresses the extracted features and mines representation embedding, but also learns domain-invariant information. Additionally, the decoder uses reconstruction loss as a constraint for feature reconstruction, prompting the model to learn representations for classification tasks. During the feature extraction and reconstruction process, the difference in feature distribution between the source and target domains is measured multiple times to enhance the discriminative accuracy of the sample classifier.  
The rest of this paper is structured as follows: In Section 2, we will specifically introduce previous related research works and the differences from the proposed method. In Section 3, the proposed method is presented in detail. In Section 4, the comparative experiments and ablation experiments are presented and analyzed. Finally, we conclude the paper in Section 5 and offer outlooks for future work.