Abstract
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 this https URL.
Abstract (translated)
监督深度学习方法在医学图像分析中取得了相当大的成功,因为这些数据集大规模、标记良好的可用性。然而,在病理诊断中创建这样的数据集对于整个Slide图像(WSIs)来说是一项挑战性的任务,因为它们的像素大小高达数百万。近年来,自监督学习(SSL)已成为减少WSIs的标注 overheads的另一种解决方案,因为它不需要训练标签。但是这些SSL方法的设计专门针对处理高分辨率WSIs,这限制了它们在学习区别性图像特征方面的表现。在本文中,我们提出了一个用于WSI肿瘤分割的双向分支 SSL框架(DSF-WSI),它可以从高分辨率WSIs中有效地学习图像特征。我们的DSF-WSI连接了两个分支,并同时学习低分辨率和高分辨率WSIs的自监督学习。此外,我们引入了一个 novel Context-Target Fusion Module(CTFM)和一个掩膜拼图任务,以 align 学到的高分辨率特征。此外,我们设计了Dense SimSiam Learning(DSL)策略,以最大化 WSIs不同视图之间的相似性,从而使学习到的表示更加高效和区别性。我们使用了两个公开数据集,乳腺癌和肝脏癌症分割任务,对方法进行了评估。实验结果表明,我们的DSF-WSI可以有效地提取稳健和高效的表示,我们通过后续微调和半监督设置进行了验证。我们提出的方法比其他任何先进的方法取得了更好的准确性。代码在此 https URL 可用。
URL
https://arxiv.org/abs/2303.11019
