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首页> 外文会议>International conference on artificial neural networks >Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network
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Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network

机译：主动学习和深度卷积神经网络对数据不平衡影响的组织病理学图像进行乳腺癌分类

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In this work, we propose an algorithm for training deep neural networks for classification of breast cancer in histopathological images affected by data unbalance with support of active learning. The output of the neural network on unlabeled samples is used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A number of low confidence samples that are selected in each iteration is manually labeled by pathologist. A threshold that decays over iteration number is used to decide which high confidence samples should be concatenated with manually labeled samples and then used in fine-tuning of convolutional neural network. The neural network can optionally be trained using weighted cross-entropy loss to better cope with bias towards the majority class.
机译：在这项工作中，我们提出了一种训练深度神经网络的算法，用于在受到主动学习支持的情况下受数据不平衡影响的组织病理学图像中对乳腺癌进行分类。神经网络在未标记样本上的输出用于计算加权信息熵。它用作不确定性评分，用于自动选择具有高置信度和低置信度的样本。病理学家手动标记每次迭代中选择的许多低置信度样本。在迭代次数上衰减的阈值用于确定应将哪些高置信度样本与手动标记的样本连接起来，然后将其用于卷积神经网络的微调。可以选择使用加权交叉熵损失来训练神经网络，以更好地应对多数群体的偏见。

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