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基于元学习的牙科图像分类与检测

Classification and detection of dental images using meta-learning.

作者信息

Yadalam Pradeep Kumar, Anegundi Raghavendra Vamsi, Alarcón-Sánchez Mario Alberto, Heboyan Artak

机构信息

Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, Tamil Nadu, India.

South Pacific Dental Institute, Chilpancingo de los Bravo 39022, Guerrero, Mexico.

出版信息

World J Clin Cases. 2024 Nov 16;12(32):6559-6562. doi: 10.12998/wjcc.v12.i32.6559.

Abstract

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input. Instead of just memorizing a task, this is accomplished through teaching a model how to learn. Algorithms for meta-learning are typically trained on a collection of training problems, each of which has a limited number of labelled instances. Multiple X-ray classification tasks, including the detection of pneumonia, coronavirus disease 2019, and other disorders, have demonstrated the effectiveness of meta-learning. Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods. Due to the high cost and lengthy collection process associated with dental imaging datasets, this is significant for dental X-ray classification jobs. The ability to train models that are more resistant to fresh input is another benefit of meta-learning.

摘要

牙科X射线的元学习是一种机器学习技术,可用于训练模型以快速且以最少输入执行新任务。这不是仅仅记住一项任务,而是通过教导模型如何学习来实现。元学习算法通常在一组训练问题上进行训练,每个问题都有数量有限的标记实例。多个X射线分类任务,包括肺炎、2019冠状病毒病和其他疾病的检测,都证明了元学习的有效性。元学习的好处是可以在对于更传统的机器学习方法来说数量太少的牙科X射线数据集上训练模型。由于与牙科成像数据集相关的高成本和冗长的收集过程,这对于牙科X射线分类工作意义重大。元学习的另一个好处是能够训练对新输入更具抗性的模型。

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