We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images.

翻译：我们提出了一种全面的方法，利用所有层次的特征抽象，旨在提高图像分类在未观测医院中的泛化性。我们的方法结合了基于增强的自监督学习，并以组织病理学场景中常见的分布偏移作为前置任务。这使得我们能够在不依赖训练标签的情况下从训练图像中提取不变特征，从而覆盖不同的抽象层次。进入下一个抽象层次，我们采用领域对齐模块来促进在不同训练医院中进一步提取不变特征。为了表示参与医院的高度特异性特征，我们训练了一个编码器来分类医院标签，独立于其诊断标签。这些编码器提取的特征随后被解耦，以最小化冗余并分离特征。这种覆盖广泛语义信息的表征，使得模型能够对来自不同分布未见过的图像展现出更强的鲁棒性。在PACS数据集（一个领域泛化基准）、通过向MHIST数据集应用组织病理学特定抖动创建的合成数据集（定义了具有不同分布偏移的不同领域），以及来自TCGA四个图像仓库的肾细胞癌数据集上的实验结果表明，我们提出的模型善于处理不同层次的图像粒度。因此，在面对新的、分布外的医院图像时，它表现出更好的泛化能力。