Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a challenging task due to their gigapixel size. In recent years, self-supervised learning (SSL) has emerged as an alternative solution to reduce the annotation overheads in WSIs, as it does not require labels for training. These SSL approaches, however, are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features. In this paper, we propose a Dual-branch SSL Framework for WSI tumour segmentation (DSF-WSI) that can effectively learn image features from multi-resolution WSIs. Our DSF-WSI connected two branches and jointly learnt low and high resolution WSIs in a self-supervised manner. Moreover, we introduced a novel Context-Target Fusion Module (CTFM) and a masked jigsaw pretext task to align the learnt multi-resolution features. Furthermore, we designed a Dense SimSiam Learning (DSL) strategy to maximise the similarity of different views of WSIs, enabling the learnt representations to be more efficient and discriminative. We evaluated our method using two public datasets on breast and liver cancer segmentation tasks. The experiment results demonstrated that our DSF-WSI can effectively extract robust and efficient representations, which we validated through subsequent fine-tuning and semi-supervised settings. Our proposed method achieved better accuracy than other state-of-the-art approaches. Code is available at https://github.com/Dylan-H-Wang/dsf-wsi.

翻译：监督式深度学习方法借助大规模高质量标注数据集的可用性，在医学图像分析领域取得了显著成功。然而，在组织病理学中为全切片图像（WSI）创建此类数据集是一项极具挑战性的任务，因其图像尺寸可达千兆像素级别。近年来，自监督学习（SSL）作为降低WSI标注开销的替代方案应运而生，其训练过程无需标签。然而，现有SSL方法并非针对处理多分辨率WSI而设计，这限制了其在学习判别性图像特征方面的性能。本文提出一种用于WSI肿瘤分割的双分支自监督学习框架（DSF-WSI），能够有效从多分辨率WSI中学习图像特征。我们的DSF-WSI连接两个分支，以自监督方式联合学习低分辨率和高分辨率WSI。此外，我们引入了一种新型上下文-目标融合模块（CTFM）和掩码拼图预任务，以对齐所学习的多分辨率特征。进一步，我们设计了密集SimSiam学习（DSL）策略，最大化WSI不同视角的相似性，使学习到的表征更加高效且具有判别性。我们使用两个公开数据集在乳腺癌和肝癌分割任务上评估了该方法。实验结果表明，我们的DSF-WSI能够有效提取鲁棒且高效的表征，后续微调和半监督设置验证了这一点。所提方法在准确性上优于其他现有最优方法。代码已在https://github.com/Dylan-H-Wang/dsf-wsi开源。