论文标题
组织病理学的内核注意变压器（KAT）全幻灯片图像分类
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
论文作者
论文摘要
对于肿瘤分级，预后分析等目的，变压器已被广泛用于组织病理学全幻灯片图像（WSI）分类。但是，在公共变压器中，在令牌上的自我注意力和位置嵌入策略的设计限制了用于GigApixixixel组织病理学病理学图像的应用的有效性和效率。在本文中，我们提出了用于组织病理学WSI分类的内核注意变压器（KAT）。令牌的信息传输是通过令牌与与WSI上一组位置锚有关的一组内核之间的交叉注意来实现的。与共同的变压器结构相比，提出的KAT可以更好地描述WSI局部区域的分层上下文信息，同时保持较低的计算复杂性。在具有2040 WSI的胃数据集上评估了所提出的方法和具有2560 WSIS的子宫内膜数据集，并与6种最先进的方法进行了比较。实验结果表明，提出的KAT在组织病理学WSI分类的任务中有效有效，并且优于最新方法。该代码可在https://github.com/zhengyushan/kat上找到。
Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits the effectiveness and efficiency in the application to gigapixel histopathology images. In this paper, we propose a kernel attention Transformer (KAT) for histopathology WSI classification. The information transmission of the tokens is achieved by cross-attention between the tokens and a set of kernels related to a set of positional anchors on the WSI. Compared to the common Transformer structure, the proposed KAT can better describe the hierarchical context information of the local regions of the WSI and meanwhile maintains a lower computational complexity. The proposed method was evaluated on a gastric dataset with 2040 WSIs and an endometrial dataset with 2560 WSIs, and was compared with 6 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI classification and is superior to the state-of-the-art methods. The code is available at https://github.com/zhengyushan/kat.
