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首页> 外文会议>International Workshop on Deep Learning for Pattern Recognition >Convolutional Neural Network-Based Classification of Histopathological Images Affected by Data Imbalance
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Convolutional Neural Network-Based Classification of Histopathological Images Affected by Data Imbalance

机译：基于卷积神经网络的组织病理学图像分类，受数据不平衡影响

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摘要

In this paper we experimentally evaluated the impact of data imbalance on the convolutional neural networks performance in the histopathological image recognition task. We conducted our analysis on the Breast Cancer Histopathological Database. We considered four phenomena associated with data imbalance: how does it affect classification performance, what strategies of preventing imbalance are suitable for histopathological data, how presence of imbalance affects the value of new observations, and whether sampling training data from a balanced distribution during data acquisition is beneficial if test data will remain imbalanced. The most important findings of our experimental analysis are the following: while high imbalance significantly affects the performance, for some of the metrics small imbalance. Sampling training data from a balanced distribution had a decremental effect, and we achieved a better performance applying a dedicated strategy of dealing with imbalance. Finally, not all of the traditional strategies of dealing with imbalance translate well to the histopathological image recognition setting.
机译：本文我们通过实验评估了数据不平衡对组织病理学图像识别任务中卷积神经网络性能的影响。我们对乳腺癌组织病理数据库进行了分析。我们考虑了与数据不平衡相关的四种现象：它如何影响分类性能，预防失衡的策略适用于组织病理数据，不平衡的存在如何影响新观察的价值，以及在数据采集期间的均衡分布采样训练数据。如果测试数据将保持不平衡，则是有益的。我们的实验分析的最重要结果如下：虽然一些度量小不平衡，但高不平衡显着影响性能。从平衡分配的采样训练数据具有递减效果，我们实现了更好的绩效，适用于处理不平衡的专用策略。最后，并非所有处理不平衡的传统战略都很好地转化为组织病理学图像识别设定。

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