注意力去稀疏化至关重要：在数字病理学表示学习中引入多样性
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
AI总结：
本文提出DiRL技术，通过细胞分割密集提取组织病理学特征并设计先验引导的密集前置任务，解决自监督模型注意力稀疏问题，使注意力更均匀分布以捕获丰富上下文表示，在多癌症类型任务中验证了有效性。
摘要：
我们提出了DiRL，一种用于组织病理学成像的多样性诱导表示学习技术。自监督学习技术，如对比和非对比方法，已被证明能够在病理学家监督有限的情况下，学习到数字化组织样本的丰富且有效的表示。我们对普通SSL预训练模型的注意力分布分析揭示了一个有洞察力的观察：注意力存在稀疏性，即模型倾向于将其大部分注意力集中在图像中的一些显著模式上。尽管由于这些显著模式本身就是感兴趣的对象，注意力稀疏性在自然图像中可能是有益的，但在数字病理学中这可能是次优的；这是因为与自然图像不同，数字病理学扫描不是以对象为中心的，而是各种空间混合生物成分的复杂表型。在这些复杂图像中，注意力多样化不足可能会导致关键信息丢失。为了解决这个问题，我们利用细胞分割来密集提取多个组织病理学特有的表示，然后为SSL提出了一个先验引导的密集前置任务，旨在匹配视图之间的多个对应表示。通过这种方式，模型学会了更紧密、更均匀地关注各种成分，从而诱导出足够的注意力多样化，以捕获上下文丰富的表示。通过对跨癌症类型的多个任务进行定量和定性分析，我们证明了我们方法的有效性，并观察到注意力分布更加全局化。
英文摘要：
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models' attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although attention sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological components. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we leverage cell segmentation to densely extract multiple histopathology-specific representations, and then propose a prior-guided dense pretext task for SSL, designed to match the multiple corresponding representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversification in attention for capturing context rich representations. Through quantitative and qualitative analysis on multiple tasks across cancer types, we demonstrate the efficacy of our method and observe that the attention is more globally distributed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
