Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization
Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization
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DOI：
10.1109/gcce56475.2022.10014057
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
M. I. Rumman;N. Ono;K. Ohuchida;M. Altaf-Ul-Amin;Ming Huang;Shigehiko Kanaya
中科院分区：
文献类型：
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作者：
M. I. Rumman;N. Ono;K. Ohuchida;M. Altaf-Ul-Amin;Ming Huang;Shigehiko Kanaya
近年来，基于深度学习概念的计算机辅助诊断已成为医学成像领域一个有吸引力的研究课题。这些工作中的大多数都使用了需要先验病理学知识的监督学习方法。然而，有必要基于无监督学习提取图像中的潜在特征，以获得新的病理发现。为此，我们实施了基于最大化互信息的无监督聚类分析，将胰腺癌病理图像分类为离散类别。
In recent years, computer-aided diagnosis based on deep learning concepts has become an attractive research topic in medical imaging. Most of these works utilized supervised learning methods that required prior pathological knowledge. However, it is necessary to extract potential features in images, based on unsupervised learning in order to obtain new pathological findings. For this reason, we implemented unsupervised cluster analysis based on maximization of mutual information to classify pathological images of pancreatic cancer into discrete categories.