Whole-slide image (WSI) analysis plays a crucial role in cancer diagnosis and treatment. In addressing the demands of this critical task, self-supervised learning (SSL) methods have emerged as a valuable resource, leveraging their efficiency in circumventing the need for a large number of annotations, which can be both costly and time-consuming to deploy supervised methods. Nevertheless, patch-wise representation may exhibit instability in performance, primarily due to class imbalances stemming from patch selection within WSIs. In this paper, we introduce Nearby Patch Contrastive Learning (NearbyPatchCL), a novel self-supervised learning method that leverages nearby patches as positive samples and a decoupled contrastive loss for robust representation learning. Our method demonstrates a tangible enhancement in performance for downstream tasks involving patch-level multi-class classification. Additionally, we curate a new dataset derived from WSIs sourced from the Canine Cutaneous Cancer Histology, thus establishing a benchmark for the rigorous evaluation of patch-level multi-class classification methodologies. Intensive experiments show that our method significantly outperforms the supervised baseline and state-of-the-art SSL methods with top-1 classification accuracy of 87.56%. Our method also achieves comparable results while utilizing a mere 1% of labeled data, a stark contrast to the 100% labeled data requirement of other approaches. Source code: https://github.com/nvtien457/NearbyPatchCL

整张切片图像（WSI）分析在癌症诊断和治疗中起着至关重要的作用，自监督学习（SSL）方法由于其在避免需要大量注释的情况下提高效率而成为宝贵的资源，尤其适用于解决WSI内部补丁选择引起的类别不平衡问题。我们提出了一种新颖的自监督学习方法Nearby Patch Contrastive Learning (NearbyPatchCL)，它利用附近补丁作为正样本，采用解耦的对比损失进行鲁棒表示学习。该方法在补丁级别多类别分类的下游任务中显著提高了性能，达到了87.56%的Top-1分类准确率，并且仅使用了1%的标注数据。

NearbyPatchCL: 利用邻近补丁进行自监督式整片图像补丁级多类别分类