Equi-ViT：用于鲁棒病理学分析的旋转等变视觉变换器
Equi-ViT: Rotational Equivariant Vision Transformer for Robust Histopathology Analysis
AI总结：
Equi-ViT通过引入等变卷积核提升ViT在病理学图像中的旋转鲁棒性和数据效率。
摘要：
视觉变换器（ViTs）因其通过自注意力机制建模长程依赖性而在计算病理学中迅速普及，解决了卷积神经网络在局部模式捕捉方面表现优异但难以处理全局上下文推理的局限性。最近的病理学专用基础模型通过利用大规模预训练进一步提升了性能。然而，标准ViTs本质上对旋转和反射等变换不具有等变性，这些在组织病理学成像中非常普遍。为了解决这一限制，我们提出了Equi-ViT，它在ViT架构的补丁嵌入阶段集成了等变卷积核，使学习到的表示具有内置的旋转等变性。Equi-ViT实现了更优的旋转一致的补丁嵌入和在图像方向上的稳定分类性能。我们在公开的结直肠癌数据集上的结果表明，整合等变补丁嵌入提高了数据效率和鲁棒性，表明等变变换器可能成为ViT在病理学应用中的更通用的骨干网络，如数字病理学基础模型。
英文摘要：
Vision Transformers (ViTs) have gained rapid adoption in computational pathology for their ability to model long-range dependencies through self-attention, addressing the limitations of convolutional neural networks that excel at local pattern capture but struggle with global contextual reasoning. Recent pathology-specific foundation models have further advanced performance by leveraging large-scale pretraining. However, standard ViTs remain inherently non-equivariant to transformations such as rotations and reflections, which are ubiquitous variations in histopathology imaging. To address this limitation, we propose Equi-ViT, which integrates an equivariant convolution kernel into the patch embedding stage of a ViT architecture, imparting built-in rotational equivariance to learned representations. Equi-ViT achieves superior rotation-consistent patch embeddings and stable classification performance across image orientations. Our results on a public colorectal cancer dataset demonstrate that incorporating equivariant patch embedding enhances data efficiency and robustness, suggesting that equivariant transformers could potentially serve as more generalizable backbones for the application of ViT in histopathology, such as digital pathology foundation models.
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。
