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arXiv 2305.04401eess.IVcs.CV

医学影像的少样本学习：方法学比较分析与形式化数学框架

Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

International Islamic University Chittagong（吉大港国际伊斯兰大学）；Islamic University（伊斯兰大学）；University of Oklahoma（俄克拉荷马大学）

Jannatul Nayem, Sayed Sahriar Hasan, Noshin Amina, Bristy Das, Md Shahin Ali, Md Manjurul Ahsan, Shivakumar Raman

备注：
Accepted for a Springer book chapter for a book title "Data-driven approaches to Medical Imaging"
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AI总结：

本文综述医学影像中的少样本学习，梳理其背景、分类与形式化数学框架，比较各类方法在医学图像分析中的应用与进展，并展望未来方向。

摘要：

深度学习已成为处理众多机器学习任务的高阶语境，并在从非结构化数据中提取特征方面展现出突破性提升。尽管这一蓬勃发展的语境正在医学图像处理领域推进，但依赖于具体问题的训练数据稀缺，已成为深度学习在医学领域便捷应用道路上的一个更大问题。为破解受限数据源，研究者开发了一种能够用更少数据解决机器学习问题的模型，称为“少样本学习（Few shot learning）”。少样本学习算法致力于通过分类和分割方法从小型数据集中提取特征，以解决数据受限问题。在医学领域，针对某些涉密疾病，经常存在可用数据集不足的情况。因此，少样本学习在这一数据稀缺领域受到广泛关注。本章阐述了少样本学习的背景与基本概述，并在此后描述了少样本学习的分类。论文还比较了随时间推移应用于医学图像分析的各类方法学路径，说明了少样本学习在医学影像实施方面的当前进展，并进一步描述了该领域在医学影像行业的未来发展空间。

英文摘要：

Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researchers have developed a model that can solve machine learning problems with fewer data called ``Few shot learning". Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset through classification and segmentation methods. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases. Therefore, Few shot learning gets the limelight in this data scarcity sector. In this chapter, the background and basic overview of a few shots of learning is represented. Henceforth, the classification of few-shot learning is described also. Even the paper shows a comparison of methodological approaches that are applied in medical image analysis over time. The current advancement in the implementation of few-shot learning concerning medical imaging is illustrated. The future scope of this domain in the medical imaging sector is further described.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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