论文标题
关于FSL方法通过域适应的概括功能：内镜肾脏石材图像分类中的案例研究
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
论文作者
论文摘要
深度学习在计算机视觉的各个领域表现出了巨大的希望，例如图像分类，对象检测和语义细分等。但是，由于数据分布变化，在数据集中训练的深度学习方法并不能很好地概括到来自其他域甚至类似数据集的数据集的深度学习方法。在这项工作中，我们建议使用基于元学习的几局学习方法来减轻这些问题。为了证明其功效，我们使用了两个具有不同内窥镜和不同采集条件的肾结石样品数据集。结果表明，这种方法确实如何通过分别在5速5-Shot和5-way 20-Shot设置中获得74.38％和88.52％的精度来处理域转移。取而代之的是，在同一数据集中，传统的深度学习（DL）方法仅获得45％的精度。
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as it has been repeatedly demonstrated, deep learning methods trained on a dataset do not generalize well to datasets from other domains or even to similar datasets, due to data distribution shifts. In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems. In order to demonstrate its efficacy, we use two datasets of kidney stones samples acquired with different endoscopes and different acquisition conditions. The results show how such methods are indeed capable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52% in the 5-way 5-shot and 5-way 20-shot settings respectively. Instead, in the same dataset, traditional Deep Learning (DL) methods attain only an accuracy of 45%.
