Whole-Slide Image Classification via Deep Feature Fusion and Unsupervised Conditional Domain Adaptation

Deep learning models have received widespread attention in pathological image classification and recognition tasks. However, their performance relies on large amounts of annotated data, which are difficult to obtain for pathological images, severely limiting model generalization. Moreover, whole-slide images (WSIs) are extremely large and must be divided into patches for processing, which often leads to the loss of global information and degrades recognition performance. To address these issues, this paper proposes a cross-domain WSI classification and recognition method based on deep feature fusion and conditional domain alignment (CTCA). The method targets unsupervised domain adaptation scenarios. It constructs a parallel architecture of transfer-pretrained networks, BreNet and Swin Transformer, to jointly extract local detail and global contextual features, achieving multi-scale and multi-perspective deep feature fusion. Subsequently, labels are introduced as conditional variables in the latent space to perform conditional domain alignment, preserving category correlations while reducing distribution discrepancies between domains. Finally, lesion regions in WSIs are visually annotated using predicted probability heatmaps. Experiments show that, under an unsupervised setting, the method effectively leverages small-scale labeled data to guide lesion recognition in unlabeled WSIs, outperforming existing unsupervised domain adaptation methods in accuracy and stability and enabling visualization of regions of interest to support clinical diagnosis.