  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.
