Srinidhi Chetan L, Kim Seung Wook, Chen Fu-Der, Martel Anne L
Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Canada.
Department of Computer Science, University of Toronto, Canada.
Med Image Anal. 2022 Jan;75:102256. doi: 10.1016/j.media.2021.102256. Epub 2021 Oct 13.
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learning unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: (i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; (ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close to or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models are made available at: https://github.com/srinidhiPY/SSL_CR_Histo.
在计算组织病理学中，使用大规模带标签数据集训练神经网络仍然是一种主导范式。然而，获取如此详尽的人工标注通常成本高昂、费力，并且容易出现观察者间和观察者内的差异。虽然最近的自监督和半监督方法可以通过学习无监督特征表示来缓解这种需求，但当有标签实例数量较少时，它们在向下游任务的泛化方面仍然存在困难。在这项工作中，我们基于两种新策略利用任务无关和任务特定的无标签数据来克服这一挑战：（i）一种自监督预训练任务，利用组织学全切片图像中潜在的多分辨率上下文线索来学习用于无监督表示学习的强大监督信号；（ii）一种新的师生半监督一致性范式，基于与任务特定无标签数据的预测一致性，学习有效地将预训练表示转移到下游任务。我们在两个分类和一个基于回归的任务（即肿瘤转移检测、组织类型分类和肿瘤细胞密度量化）的三个组织病理学基准数据集上进行了广泛的验证实验。在有限标签数据的情况下，所提出的方法产生了显著的改进，接近甚至优于其他当前最先进的自监督和监督基线。此外，我们通过实验表明，自监督预训练特征的自训练思想是在标准基准上改进任务特定半监督学习的有效方法。代码和预训练模型可在以下网址获取：https://github.com/srinidhiPY/SSL_CR_Histo 。
