计算机科学
可解释性
人工智能
卷积神经网络
背景(考古学)
图形
模式识别(心理学)
空间语境意识
机器学习
深度学习
像素
上下文图像分类
图像(数学)
理论计算机科学
古生物学
生物
作者
Meiyan Liang,Qinghui Chen,Bo Li,Lin Wang,Ying Wang,Yu Zhang,Ru Wang,Xingyu Jiang,Cunlin Zhang
标识
DOI:10.1016/j.cmpb.2022.107268
摘要
Whole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computational pathology that requires context-aware representations of histological features to adequately infer nidus. The existing weakly supervised learning methods mainly treat different locations in the slide as independent regions and cannot learn potential nonlinear interactions between instances based on i.i.d assumption, resulting in the model unable to effectively utilize context-ware information to predict the labels of WSIs and locate the region of interest (ROI). Here, we propose an interpretable classification model named bidirectional Attention-based Multiple Instance Learning Graph Convolutional Network (ABMIL-GCN), which hierarchically aggregates context-aware features of instances into a global representation in a topology fashion to predict the slide labels and localize the region of lymph node metastasis in WSIs. We verified the superiority of this method on the Camelyon16 dataset, and the results show that the average predicted ACC and AUC of the proposed model after flooding optimization can reach 90.89% and 0.9149, respectively. The average accuracy and ACC score are improved by more than 7% and 4% compared with the existing state-of-the-art algorithms. The results demonstrate that context-aware GCN outperforms existing weakly supervised learning methods by introducing spatial correlations between the neighbor image patches, which also addresses the ‘accuracy-interpretability trade-off’ problem. The framework provides a novel paradigm for the clinical application of computer-aided diagnosis and intelligent systems.
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