# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 2 Related Works  
### Semi-Supervised Learning for Image Classification  
Semi-supervised learning (SSL) has become a vital approach for image classification, particularly in scenarios with limited labeled data, such as medical imaging, where high-quality annotations are costly and time-intensive to acquire. SSL leverages a small labeled dataset alongside a larger pool of unlabeled data to improve model performance. Traditional SSL methods include self-training [15, 16], which generates pseudo-labels for unlabeled data using the model's predictions and incorporates them into training, co-training [17], which uses complementary views or models to refine pseudo labels, and graph-based approaches, which propagate labels through similarity graphs between samples.  
With the rise of deep learning, SSL methods have evolved significantly. Fix-Match [18; 19] combines pseudo-labeling with consistency regularization, enforcing consistent predictions across augmented versions of unlabeled data, achieving strong performance in image classification. MixMatch [20] integrates data augmentation and MixUp to enhance model generalization by blending labeled and unlabeled samples. Unsupervised Data Augmentation (UDA) [21] further emphasizes augmentation invariance, using diverse transformations to improve robustness in SSL settings.  
SSL has been increasingly applied to address data scarcity in medical image classification. For breast cancer histopathology image classification, [PERSON] et al. [22] proposed an SSL framework that leverages unlabeled data to enhance model robustness, achieving improved performance on datasets with limited annotations. [PERSON] et al. [23] combined SSL with transfer learning, applying it to medical image classification tasks with constrained labeled data, demonstrating its effectiveness in handling diverse medical imaging modalities. These works highlight the potential of SSL in medical imaging. Still, they often rely on generic architectures, leaving room for specialized models tailored to histopathology images, as addressed in our work with the Dynamic Cross-Scale Swin Transformer (DCS-ST), which integrates pseudo-labeling and semi-supervised learning to tackle limited annotations in breast cancer classification.  
Recently, GAN-based data augmentation methods have been widely adopted to mitigate data scarcity and class imbalance in biomedical imaging, including modalities such as fMRI and mammography [24]. Among these, [PERSON] et al. [25] proposed GANSO, which integrates Generative Adversarial Networks (GANs) with Markov Random Fields (MRFs) to produce spatially coherent synthetic samples even under extremely small dataset conditions. Similarly, [PERSON] et al. [26] introduced ISING-GAN, a GAN-based augmentation approach incorporating an Ising model prior to impose spatial constraints during training, improving the realism of both images and annotations. [PERSON] and [PERSON] [27] further proposed OBGAN, which focuses on generating minority class samples near the decision boundary, enhancing model robustness in imbalanced learning scenarios. These advanced oversampling strategies are closely aligned with the challenges addressed in this work and could serve as promising directions to complement our current semi-supervised learning framework.