论文标题
使用图形卷积神经网络的组织病理学图像可视化
Visualization for Histopathology Images using Graph Convolutional Neural Networks
论文作者
论文摘要
随着对医学图像中计算机辅助诊断的深度学习的增加，对深度学习模型的黑盒性质的批评也正在上升。医学界需要可解释的模型，以进行尽职调查和推进对疾病和治疗机制的理解。特别是在组织学上，尽管细胞级别和细胞之间的空间关系有丰富的细节，但很难修改卷积神经网络以指出相关的视觉特征。我们采用一种模拟组织学组织作为核图的方法，并基于注意机制和节点闭塞来开发图形卷积网络框架。提出的方法突出了每个细胞核在整个扫描图像中的相对贡献。我们对经过训练的网络的可视化，以区分侵入性和原位乳腺癌，而格里森3和4前列腺癌产生了可解释的视觉图，这与我们对对专家对其诊断非常重要的结构的理解非常吻合。
With the increase in the use of deep learning for computer-aided diagnosis in medical images, the criticism of the black-box nature of the deep learning models is also on the rise. The medical community needs interpretable models for both due diligence and advancing the understanding of disease and treatment mechanisms. In histology, in particular, while there is rich detail available at the cellular level and that of spatial relationships between cells, it is difficult to modify convolutional neural networks to point out the relevant visual features. We adopt an approach to model histology tissue as a graph of nuclei and develop a graph convolutional network framework based on attention mechanism and node occlusion for disease diagnosis. The proposed method highlights the relative contribution of each cell nucleus in the whole-slide image. Our visualization of such networks trained to distinguish between invasive and in-situ breast cancers, and Gleason 3 and 4 prostate cancers generate interpretable visual maps that correspond well with our understanding of the structures that are important to experts for their diagnosis.
