数字病理学中的可解释且位置感知学习
Explainable and Position-Aware Learning in Digital Pathology
- Marquette University（马凯特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对图学习方法处理全切片图像时忽略位置信息的问题，提出结合样条CNN位置嵌入与图注意力的癌症分类方法，在前列腺癌、肾癌数据集上性能优于主流方法，还通过梯度显著性映射实现模型可解释性。
AI中文摘要：
将全切片图像（WSI）编码为图结构具有充分的合理性，因为这使得我们能够完整表示具有十亿像素级分辨率的WSI，以用于图学习。为此，可将WSI分割为更小的图像块，这些图像块构成图的节点。随后，基于图的学习方法可被用于癌症的分级与分类。相邻节点间的消息传递是基于图的学习方法的核心基础。然而，这类方法并未考虑任何图像块的位置信息，若两个图像块处于拓扑同构的邻域中，它们的嵌入向量会彼此高度相似。在本研究中，我们结合位置嵌入与图注意力机制，开展基于WSI的癌症分类任务。为了在图分类中表示节点的位置嵌入，所提出的方法采用了样条卷积神经网络（CNN）。随后，我们使用前列腺癌和肾癌分级的WSI数据集对该算法进行了测试。将所提方法与癌症诊断和分级领域的主流方法进行对比，结果验证了其性能的提升。识别WSI中的癌变区域是癌症诊断中的另一项关键任务。本研究还探讨了所提模型的可解释性问题，采用一种基于梯度的可解释性方法生成WSI的显著性映射。该方法可用于探查WSI中对癌症诊断起关键作用的区域，从而使所提模型具备可解释性。
英文摘要：
Encoding whole slide images (WSI) as graphs is well motivated since it makes it possible for the gigapixel resolution WSI to be represented in its entirety for the purpose of graph learning. To this end, WSIs can be broken into smaller patches that represent the nodes of the graph. Then, graph-based learning methods can be utilized for the grading and classification of cancer. Message passing among neighboring nodes is the foundation of graph-based learning methods. However, they do not take into consideration any positional information for any of the patches, and if two patches are found in topologically isomorphic neighborhoods, their embeddings are nearly similar to one another. In this work, classification of cancer from WSIs is performed with positional embedding and graph attention. In order to represent the positional embedding of the nodes in graph classification, the proposed method makes use of spline convolutional neural networks (CNN). The algorithm is then tested with the WSI dataset for grading prostate cancer and kidney cancer. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance. The identification of cancerous regions in WSIs is another critical task in cancer diagnosis. In this work, the explainability of the proposed model is also addressed. A gradient-based explainbility approach is used to generate the saliency mapping for the WSIs. This can be used to look into regions of WSI that are responsible for cancer diagnosis thus rendering the proposed model explainable.
