We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation

提出了一种钩合卷积神经网络（HookNet）模型，用于组织病理学全扫描图像的语义分割，包括多个编码器 - 解码器分支，通过多个解析度的同心圆形补丁将上下文和细节结合起来，并通过钩机制组合中间表示。在乳腺癌组织类型分割和肺癌第三淋巴结结构和生发中心分割任务中，展示了与单分辨率U-Net模型和最近公开的多分辨率模型相比，HookNet的卓越性能。

HookNet: 多分辨率卷积神经网络用于组织病理学全扫描图像语义分割