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.
翻译:在生物医学显微镜和临床医学中,前的工作重点是自我监督的代表学习方法(SSL)方法,例如歧视,直接用于图像补丁,或从用于癌症诊断的GGAPixel整流图(WSIs)中取样的现场。然而,这一战略是有限的,因为它:(1) 假设来自同一病人的补丁是独立的,(2) 忽视临床生物医学显微镜和临床医学的病变偏差等级,(3) 需要强大的数据增强,从而降低下游性能。 重要的是,从病人肿瘤的WSI中抽取的补丁是一套不同的图像例子,它们可以捕捉到癌症诊断的根基。这个数据驱动的HIDisc是利用临床生物医学显性显性显性显性显性显性显性显性显性显性显性显性显性,我们用两种直观性显性显性显性显性显性显性显性显性显性显性显性显性显性显性直观性数据,我们用了两个直观性直观性直观性显性直观性直观性显性显性显性显性显性显性显性显性显性显性显性显性显性显性显性显性显性显性显性数据。</s>