论文标题
CD-NET：使用锥体上下文网络学习组织病理学表示学习
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
论文作者
论文摘要
从整个幻灯片组织学图像（WSIS）中提取丰富的表型信息，例如细胞密度和排列，需要对大型视野进行分析，即更多的信息信息。这可以通过分析较低分辨率的数字幻灯片来实现。在更高分辨率的细节上，缺少潜在的缺点。为了共同利用来自多种分辨率的互补信息，我们提出了一种新型的基于变压器的金字塔上下文尾尾网络（CD-NET）。 CD-NET通过共同培训提出的上下文和细节模块来利用WSI锥体结构，这些上下文和细节模块可在多个分辨率的输入上运行。模块之间的残差连接使联合训练范式在学习WSIS的自制表示。 CD-NET的功效在对鳞状细胞癌的肺腺癌分类中得到了证明。
Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail Modules, which operate on inputs from multiple resolutions. The residual connections between the modules enable the joint training paradigm while learning self-supervised representation for WSIs. The efficacy of CD-Net is demonstrated in classifying Lung Adenocarcinoma from Squamous cell carcinoma.
