论文标题
S $^3 $ R：高光谱组织病理学图像分类的自我监督光谱回归
S$^3$R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
论文作者
论文摘要
HSI受益于高光谱图像（HSI）中丰富而详细的光谱信息，为各种医学应用（例如计算病理学）提供了巨大的潜力。但是，缺乏足够的注释数据和HSI的高时光尺寸通常会使分类网络容易过度fit。因此，学习可以转移到下游任务的一般表示是必须的。据我们所知，没有针对HSIS的组织病理学设计适当的自我监管的预训练方法。在本文中，我们介绍了一种有效，有效的自我监督光谱回归（S $^3 $ R）方法，该方法利用了HSI光谱域中的低等级特征。更具体地说，我们建议学习一组线性系数，这些系数可通过掩盖这些频段来通过其余的频段来代表一个频段。然后，通过使用学习的系数重新恢复剩余的频段来恢复频段。设计了两个前文本任务：（1）S $^3 $ r-CR，它会回归线性系数，以便预先训练的模型了解HSIS的固有结构以及不同形态的病理特征； （2）S $^3 $ R-BR，它回归了失踪的频段，使模型学习了HSIS的整体语义。与先前的艺术相比，重点是自然图像的对比度学习方法，S $^3 $ r收敛至少3倍，并且在转移到HSI分类任务时，准确性高达14％。
Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression (S$^3$R) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. Then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1)S$^3$R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies; (2)S$^3$R-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, S$^3$R converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.
