Annotated images and ground truth for the diagnosis of rare and novel diseases are scarce. This is expected to prevail, considering the small number of affected patient population and limited clinical expertise to annotate images. Further, the frequently occurring long-tailed class distributions in skin lesion and other disease classification datasets cause conventional training approaches to lead to poor generalization due to biased class priors. Few-shot learning, and meta-learning in general, aim to overcome these issues by aiming to perform well in low data regimes. This paper focuses on improving meta-learning for the classification of dermoscopic images. Specifically, we propose a baseline supervised method on the meta-training set that allows a network to learn highly representative and generalizable feature embeddings for images, that are readily transferable to new few-shot learning tasks. We follow some of the previous work in literature that posit that a representative feature embedding can be more effective than complex meta-learning algorithms. We empirically prove the efficacy of the proposed meta-training method on dermoscopic images for learning embeddings, and show that even simple linear classifiers trained atop these representations suffice to outperform some of the usual meta-learning methods.
翻译:用于诊断稀有和新出现疾病的附加说明的图像和地面真相很少。考虑到受影响病人人数少,临床专业知识有限,因此预计这种情况会普遍存在。此外,皮肤病和其他疾病分类数据集中经常出现的长尾类分布导致常规培训方法导致偏向类前科导致的概括化不力。少见的学习和一般的元学习旨在通过在低数据制度中取得良好效果来克服这些问题。本文件侧重于改进脱热图像分类的元学习。具体地说,我们提议在元培训套件上采用基线监督方法,使网络能够学习具有高度代表性和可普及的图像嵌入功能,这些功能很容易被转用于新的微小的学习任务。我们遵循以往的一些文献工作,其中认为有代表性的嵌入功能比复杂的元学习算法更有效。我们从经验上证明拟议的关于脱热科图像的元培训方法对学习嵌入效果有效,并表明在这些图案上培训的简单线性分类方法足以超越一些常规的元学习方法。