可解释性
计算机科学
人工智能
深度学习
分割
深层神经网络
模式识别(心理学)
机器学习
人工神经网络
领域(数学)
标记数据
过程(计算)
数学
操作系统
纯数学
作者
Yixiao Zhang,Adam Kortylewski,Qing Liu,Seyoun Park,Ben Green,Elizabeth L. Engle,Guillermo Almodovar,Ryan Walk,Sigfredo Soto-Diaz,Janis M. Taube,A. S. Szalay,Alan Yuille
标识
DOI:10.1007/978-3-031-16961-8_15
摘要
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histo-pathology dataset. We introduce a light-weight and interpretable model for nuclei detection and weakly-supervised segmentation. It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset. Besides, it is a generative compositional model that first locates parts of nucleus, then learns the spatial correlation of the parts to further locate the nucleus. This process brings interpretability in its prediction. Empirical results on an in-house dataset show that in detection, the proposed method achieved comparable or better performance than its deep network counterparts, especially when the annotated data is limited. It also outperforms popular weakly-supervised segmentation methods. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.
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