论文标题
带有深层隔离森林的皮肤病变图像的分布外检测
Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest
论文作者
论文摘要
在本文中，我们研究了皮肤病图像中分布外检测的问题。公开可用的医疗数据集通常有限数量的病变类（例如HAM10000具有8个病变类）。但是，存在数千种临床鉴定的疾病。因此，重要的是，如果训练数据中未有病变可以区分。为了实现这一目标，我们提出了DeepIF，这是一种非参数隔离森林方法，结合了深卷积网络。我们进行了全面的实验，以将我们的DEEPIF与三个基线模型进行比较。结果表明，我们提出的方法对检测异常皮肤病变的任务的最新表现。
In this paper, we study the problem of out-of-distribution detection in skin disease images. Publicly available medical datasets normally have a limited number of lesion classes (e.g. HAM10000 has 8 lesion classes). However, there exists a few thousands of clinically identified diseases. Hence, it is important if lesions not in the training data can be differentiated. Toward this goal, we propose DeepIF, a non-parametric Isolation Forest based approach combined with deep convolutional networks. We conduct comprehensive experiments to compare our DeepIF with three baseline models. Results demonstrate state-of-the-art performance of our proposed approach on the task of detecting abnormal skin lesions.
