@article{Li2026, 
author = {Junjian Li and Hulin Kuang and Jin Liu and Hailin Yue and Jianxin Wang},
title = {Domain-Specific Self-Supervised Contrastive Learning with Contrast-Aware Pair Refinement for Pathological Image Analysis},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {3},
pages = {705-718},
keywords = {data augmentation, domain knowledge, self-supervised contrastive learning},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020072},
doi = {10.26599/BDMA.2025.9020072},
abstract = {Deep learning offers notable promise for computational pathology, but its performance is constrained by the need for extensively annotated datasets, which are costly and laborious to produce. Self-Supervised Learning (SSL) provides an effective paradigm for learning discriminative representations from unannotated pathological images. However, existing SSL methods often overlook domain-specific characteristics of pathological images and suffer from the adverse effects of low-quality negative samples, leading to sub-optimal feature representations for downstream tasks. To overcome these limitations, we propose a novel Domain-Specific Self-supervised Contrastive Learning (DSSCL) framework, which incorporates two novel components: (1) a Stain-Separation Based Data Augmentation (SSDA) module that enhances stain-aware representation learning by fusing stain-separated components with original hematoxylin and eosin images, and (2) a Contrast-Aware Pair Refinement (CAPR) module that improves feature discriminability by filtering potential positives and mining hard negatives, thereby mitigating the influence of low-quality negatives. Extensive experiments demonstrate that DSSCL achieves comparable accuracy in classification tasks using only 0.1% labeled data compared to a network fine-tuned from ImageNet with 10% labeled data, while also delivering competitive performance in detection and segmentation tasks, underscoring its effectiveness in learning transferable and robust feature representations across diverse downstream tasks. The code is available at https://github.com/junjianli106/DSSCL.}
}