人工智能
卷积神经网络
分割
间质细胞
计算机科学
模式识别(心理学)
深度学习
特征(语言学)
图像分割
图像(数学)
计算机视觉
病理
医学
语言学
哲学
作者
Zahraa Al-Milaji,Ilker Ersoy,Adel Hafiane,Kannappan Palaniappan,Filiz Bunyak
标识
DOI:10.1016/j.patrec.2017.09.015
摘要
Initiation, progression, and therapeutic response in cancer are largely influenced by tumor microenvironment. Segmentation of tumor into epithelial vs. stromal regions constitutes the first step for the study of tumor microenvironment and its effects on disease progression. This paper proposes a new method for stromal vs. epithelial tissue identification from images of H&E stained specimens. The proposed method integrates convolutional neural networks (CNN) based supervised classification with unsupervised image segmentation. The scheme combines the strengths of deep learning (feature learning and classification) with the boundary localization accuracy of image segmentation for enhanced performance. Our experimental results on Stanford Tissue Microarray Database show that integration of CNN classification with explicit image segmentation leads to better adherence of identified class boundaries to actual tissue boundaries and improves the classification accuracy.
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