论文标题
几次射击学习以对临床内窥镜图进行分类的添加角度边缘
Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
论文作者
论文摘要
内窥镜检查是一种广泛使用的成像方式，用于诊断和治疗空心器官中的疾病，例如胃肠道，肾脏和肝脏。但是，由于各种临床中心的不同模式和使用不同成像方案的使用，在概括深度学习模型时会面临重大挑战。此外，来自不同临床中心的大型数据集的组装可以引入巨大的标签偏差，从而使任何学习的模型无法使用。同样，当使用具有罕见模式的图像的新模式或存在时，需要大量的相似图像数据及其相应的标签来训练这些模型。在这项工作中，我们建议采用几种需要较少培训数据的学习方法，可用于预测从看不见的数据集中的测试样本类别的标签类别。我们在几次学习环境中提出了一个新型的添加角度缘度量。我们将我们的方法与大量多中心，多轨和多模式内窥镜数据的几种已建立方法进行了比较。所提出的算法的表现优于现有的最新方法。
Endoscopy is a widely used imaging modality to diagnose and treat diseases in hollow organs as for example the gastrointestinal tract, the kidney and the liver. However, due to varied modalities and use of different imaging protocols at various clinical centers impose significant challenges when generalising deep learning models. Moreover, the assembly of large datasets from different clinical centers can introduce a huge label bias that renders any learnt model unusable. Also, when using new modality or presence of images with rare patterns, a bulk amount of similar image data and their corresponding labels are required for training these models. In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset. We propose a novel additive angular margin metric in the framework of prototypical network in few-shot learning setting. We compare our approach to the several established methods on a large cohort of multi-center, multi-organ, and multi-modal endoscopy data. The proposed algorithm outperforms existing state-of-the-art methods.
