Whole slide imaging is fundamental to biomedical microscopy and computational
pathology. However, whole slide images (WSIs) present a complex computer vision
challenge due to their gigapixel size, diverse histopathologic features,
spatial heterogeneity, and limited/absent data annotations. These challenges
highlight that supervised training alone can result in suboptimal whole slide
representations. Self-supervised representation learning can achieve
high-quality WSI visual feature learning for downstream diagnostic tasks, such
as cancer diagnosis or molecular genetic prediction. Here, we present a general
self-supervised whole slide learning (S3L) framework for gigapixel-scale
self-supervision of WSIs. S3L combines data transformation strategies from
transformer-based vision and language modeling into a single unified framework
to generate paired views for self-supervision. S3L leverages the inherent
regional heterogeneity, histologic feature variability, and information
redundancy within WSIs to learn high-quality whole-slide representations. We
benchmark S3L visual representations on two diagnostic tasks for two biomedical
microscopy modalities. S3L significantly outperforms WSI baselines for cancer
diagnosis and genetic mutation prediction. Additionally, S3L achieves good
performance using both in-domain and out-of-distribution patch encoders,
demonstrating good flexibility and generalizability.

自我监督整个切片学习（S3L）框架可以通过利用整个切片的固有区域异质性、组织学特征变异性和信息冗余性来学习高质量的整个切片表示，用于癌症诊断和基因突变预测等诊断任务，并且在两种生物医学显微镜条件下显著优于基线模型。

自我监督学习全尺寸切片表示的框架

A self-supervised framework for learning whole slide representations

Learning high-quality, self-supervised, visual representations is essential
to advance the role of computer vision in biomedical microscopy and clinical
medicine. Previous work has focused on self-supervised representation learning
(SSL) methods developed for instance discrimination and applied them directly
to image patches, or fields-of-view, sampled from gigapixel whole-slide images
(WSIs) used for cancer diagnosis. However, this strategy is limited because it
(1) assumes patches from the same patient are independent, (2) neglects the
patient-slide-patch hierarchy of clinical biomedical microscopy, and (3)
requires strong data augmentations that can degrade downstream performance.
Importantly, sampled patches from WSIs of a patient's tumor are a diverse set
of image examples that capture the same underlying cancer diagnosis. This
motivated HiDisc, a data-driven method that leverages the inherent
patient-slide-patch hierarchy of clinical biomedical microscopy to define a
hierarchical discriminative learning task that implicitly learns features of
the underlying diagnosis. HiDisc uses a self-supervised contrastive learning
framework in which positive patch pairs are defined based on a common ancestry
in the data hierarchy, and a unified patch, slide, and patient discriminative
learning objective is used for visual SSL. We benchmark HiDisc visual
representations on two vision tasks using two biomedical microscopy datasets,
and demonstrate that (1) HiDisc pretraining outperforms current
state-of-the-art self-supervised pretraining methods for cancer diagnosis and
genetic mutation prediction, and (2) HiDisc learns high-quality visual
representations using natural patch diversity without strong data
augmentations.

本文提出了一种名为 HiDisc 的自我监督对比学习框架，用于学习临床生物医学显微镜图像中肿瘤的高质量视觉表示，结果表明 HiDisc 预训练方法在癌症诊断和基因突变预测方面优于当前最先进的自我监督预训练方法，并且使用自然数据多样性学习高质量的视觉表达。