Self‐Supervised Transfer Learning of Cross‐Domains Histopathological Images for Cancer Diagnosis

ABSTRACT Whole‐slide imaging assisted by deep learning has been employed to help the digital pathology, while limited by the scarcity of paired label data. To address this issue, a novel self‐supervised image modeling framework, PathMAE, is proposed to effectively enlarge the labeled dataset in a cross‐domain way, where cross‐dataset and even cross‐disease histopathological images can be used for model training. PathMAE integrates masked image modeling and contrastive learning to effectively learn transferable visual representations from unlabeled WSIs. The framework comprises two key components: a Swin‐Transformer‐based encoder‐decoder (SMED) with a window‐masking strategy for local feature reconstruction, and a Dynamic Memory Contrastive Learning (DMCL) module for enhancing global semantic alignment via memory‐guided feature comparison. Experimental results on three public histopathology datasets demonstrate the robustness and generalizability of the proposed method. In cross‐disease transfer (BreakHis → Osteosarcoma), PathMAE achieved 97.15% accuracy and 99.03% AUC; in cross‐dataset transfer (BreakHis → Camelyon16), it obtained 84.67% accuracy and 88.04% AUC. These findings validate the capability of PathMAE as a scalable and domain‐adaptive image analysis framework, offering new potential for building reliable computational pathology systems under limited supervision.