Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This results in a significant class imbalance between lesion and background. From recent literature, it is known that class imbalance may negatively affect the performance of CNN classification. However, very few research exists in the context of lesion detection. In this work, we propose a two-stage deep learning framework able to deal with the high class imbalance encountered during training of small lesion detectors. First, we train a deep cascade (DC) of long sequences of decision trees with an algorithm designed to handle unbalanced data that also drastically reduces the number of background samples reaching the final stage. The remaining samples are fed to a CNN, whose training benefits from both rebalance and hard mining done by the DC. We evaluated DC-CNN on two severely unbalanced classification problems: microcalcification detection and microaneurysm detection. In both cases, DC-CNN outperformed the CNNs trained with commonly used methods for addressing class imbalance such as oversampling, undersampling, hard mining, cost sensitive learning, and one-class classification. The DC-CNN was also ∼10x faster than CNN at test time.

中文翻译：

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解决深度学习中的班级不平衡问题，以便对医学图像进行小病变检测。

利用卷积神经网络（CNN）的深度学习方法已导致对医学图像的自动理解取得了巨大进步。但是，在许多医学图像分类任务中，病变仅占据图像的几个像素。这导致病变和背景之间明显的类别失衡。从最近的文献中知道，类别不平衡可能会对CNN分类的性能产生负面影响。然而，在病灶检测的背景下很少有研究。在这项工作中，我们提出了一个两阶段的深度学习框架，该框架能够处理在小型病变检测器训练过程中遇到的高级不平衡问题。第一，我们使用一种旨在处理不平衡数据的算法来训练决策树的长序列的深层级联（DC），该算法还可以大大减少到达最终阶段的背景样本的数量。其余的样本将被馈送到CNN，其训练得益于DC进行的重新平衡和辛苦开采。我们在两个严重不平衡的分类问题上评估了DC-CNN：微钙化检测和微动脉瘤检测。在这两种情况下，DC-CNN均优于使用常用方法解决类别不平衡问题而训练的CNN，例如过采样，欠采样，硬挖掘，成本敏感型学习和一类分类。在测试时，DC-CNN也比CNN快10倍。他们的培训得益于区议会的重新平衡和辛勤工作。我们在两个严重不平衡的分类问题上评估了DC-CNN：微钙化检测和微动脉瘤检测。在这两种情况下，DC-CNN均优于使用常用方法解决类别不平衡问题而训练的CNN，例如过采样，欠采样，硬挖掘，成本敏感型学习和一类分类。在测试时，DC-CNN也比CNN快10倍。他们的培训得益于区议会的重新平衡和辛勤工作。我们在两个严重不平衡的分类问题上评估了DC-CNN：微钙化检测和微动脉瘤检测。在这两种情况下，DC-CNN均优于使用常用方法解决类别不平衡问题而训练的CNN，例如过采样，欠采样，硬挖掘，成本敏感型学习和一类分类。在测试时，DC-CNN也比CNN快10倍。