论文标题
meta-svdd：癌症组织学图像中一级分类的概率元学习
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
论文作者
论文摘要
为了培训强大的深度学习模型，通常需要在培训数据中平衡的类别集。然而，在医疗领域中获得的数据经常包含大量健康患者，而较小的阳性异常病例。此外，阳性样本的注释需要医疗领域专家的耗时意见。这种情况将为单级分类类型的方法提出希望。在这项工作中，我们提出了一个通用的组织学分类模型，该模型同时在多个组织学数据集上进行了元训练，并且可以应用于新任务而无需昂贵的重新训练。病理领域专家可以很容易地使用该模型，并可能用于筛查目的。
To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an abundance of healthy patients, versus a small variety of positive, abnormal cases. Moreover, the annotation of a positive sample requires time consuming input from medical domain experts. This scenario would suggest a promise for one-class classification type approaches. In this work we propose a general one-class classification model for histology, that is meta-trained on multiple histology datasets simultaneously, and can be applied to new tasks without expensive re-training. This model could be easily used by pathology domain experts, and potentially be used for screening purposes.
