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

翻译：全切片图像（WSI）分析在癌症诊断和治疗中起着关键作用。为应对这一关键任务的需求，自监督学习（SSL）方法已成为一种宝贵资源，因其能够高效避免对大量标注的需求——而采用监督方法则需要大量标注，既昂贵又耗时。然而，补丁级表示可能因WSI内补丁选择导致的类别不平衡而表现出性能不稳定性。本文提出邻近补丁对比学习（NearbyPatchCL），这是一种新颖的自监督学习方法，利用邻近补丁作为正样本和去耦对比损失来实现稳健的表示学习。我们的方法在涉及补丁级多类别分类的下游任务中展现出显著的性能提升。此外，我们基于犬皮肤癌组织学来源的WSI整理了一个新数据集，从而为补丁级多类别分类方法的严格评估建立了基准。大量实验表明，我们的方法显著优于监督基线方法和最先进的SSL方法，Top-1分类准确率达到87.56%。同时，我们的方法仅使用1%的标注数据即可取得可比结果，而其他方法需要100%的标注数据。源代码：https://github.com/nvtien457/NearbyPatchCL