论文标题
临床：针对性不平衡的医学图像分类的有针对性的积极学习
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
论文作者
论文摘要
在医学数据集中培训深度学习模型，这些模型在所有课程中都表现良好是一项具有挑战性的任务。通常，由于医疗数据带来的自然级别不平衡问题，在某些类别上会获得次优的性能。解决此问题的有效方法是使用有针对性的主动学习，在该学习中，我们将数据点添加到属于稀有类别的培训数据中。但是，现有的主动学习方法无效地针对医疗数据集中的稀有类别。在这项工作中，我们提出了一个临床（用于医学图像分类不平衡的有目的的主动学习），该框架使用了子模块化信息功能作为获取功能，以从罕见类别中挖掘关键数据点。我们将我们的框架应用于各种现实的类不平衡方案的广泛医学成像数据集 - 即二进制不平衡和长尾不平衡。我们表明，通过获取属于稀有类别的各种数据点，临床表现优于最新的主动学习方法。
Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data. An effective way to tackle this problem is by using targeted active learning, where we iteratively add data points to the training data that belong to the rare classes. However, existing active learning methods are ineffective in targeting rare classes in medical datasets. In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced medICal imAge cLassification) a framework that uses submodular mutual information functions as acquisition functions to mine critical data points from rare classes. We apply our framework to a wide-array of medical imaging datasets on a variety of real-world class imbalance scenarios - namely, binary imbalance and long-tail imbalance. We show that Clinical outperforms the state-of-the-art active learning methods by acquiring a diverse set of data points that belong to the rare classes.
