An unsupervised feature learning framework for basal cell carcinoma image analysis
An unsupervised feature learning framework for basal cell carcinoma image analysis
复制标题
DOI：
10.1016/j.artmed.2015.04.004
复制
发表时间：
2015-06-01
影响因子：
7.5
通讯作者：
Gonzalez, Fabio A.
中科院分区：
文献类型：
--
作者：
Arevalo, John;Cruz-Roa, Angel;Gonzalez, Fabio A.
目的：研究组织病理学图像中基底细胞癌的自动检测问题。特别是，它提出了一个框架，学习的图像表示在一个无监督的方式和可视化的判别功能支持的学习model.Materials和方法：本文提出了一个集成的无监督特征学习（UFL）框架组织病理学图像分析，包括三个主要阶段：（1）使用不同策略的局部（补丁）表示学习（稀疏自动编码器，重建独立分量分析和地形独立分量分析（TICA），（2）使用特征袋表示或卷积神经网络的全局（图像）表示学习，以及（3）视觉解释层，以突出显示模型检测到的最具鉴别力的区域。集成的无监督特征学习框架进行了详尽的评估，在一个组织病理学图像数据集BCC diagnosis.Results：实验评估产生了98.1%的分类性能，在接收器工作特征曲线下的面积方面，所提出的框架优于7%的最先进的离散余弦变换基于块的表示。所提出的基于UFL表示的方法优于用于BCC检测的最新方法。由于其视觉解释层，该方法能够突出有区别的组织区域，提供更好的诊断支持。在测试的不同UFL策略中，TICA学习的特征表现出最好的性能，这要归功于其捕获低级别不变性的能力，这是问题本质所固有的。(C)2015 Elsevier B.V.版权所有。
Objective: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model.Materials and methods: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis.Results: The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation.Conclusions: The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem. (C) 2015 Elsevier B.V. All rights reserved.