[论文解读] A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

Pengyi Zhang, Yunxin Zhong+3 more|arXiv (Cornell University)|Aug 1, 2019

AI in cancer detection参考文献 95被引用 33

一句话总结

关于生物医学图像分析中关键的小样本学习（SSL）技术的综述，分为五类，并包括有助于临床决策的模型解释方法。

ABSTRACT

The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of large-scale labelled samples. However, in many cases of biomedical image analysis, deep learning techniques suffer from the small sample learning (SSL) dilemma caused mainly by lack of annotations. To be more practical for biomedical image analysis, in this paper we survey the key SSL techniques that help relieve the suffering of deep learning by combining with the development of related techniques in computer vision applications. In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are important to clinical decision making. We survey the key SSL techniques by dividing them into five categories: (1) explanation techniques, (2) weakly supervised learning techniques, (3) transfer learning techniques, (4) active learning techniques, and (5) miscellaneous techniques involving data augmentation, domain knowledge, traditional shallow methods and attention mechanism. These key techniques are expected to effectively support the application of deep learning in clinical biomedical image analysis, and furtherly improve the analysis performance, especially when large-scale annotated samples are not available. We bulid demos at https://github.com/PengyiZhang/MIADeepSSL.

研究动机与目标

• 由于注释有限，推动在生物医学图像分析中使用 SSL 的必要性。
• 总结用于在小数据集上提升深度学习性能的主要 SSL 技术。
• 将 SSL 方法分为五类，以指导临床环境中的实践。
• 突出对临床决策和模型透明性至关重要的解释方法。

提出的方法

• 将 SSL 技术分类为五类：解释技术、弱监督学习、迁移学习、主动学习和杂项技术。
• 讨论数据增强、领域知识、传统浅层方法和注意力机制等作为辅助方法。
• 强调将解释方法整合以支持临床决策。
• 构建一个框架以加速生物医学影像中深度学习的临床应用。
• 通过在线 URL 提供演示以说明概念。

实验结果

研究问题

• RQ1 在注释数据稀缺时，生物医学图像分析存在哪些 SSL 技术？
• RQ2 解释方法以及其他辅助技术（数据增强、领域知识、传统浅层方法、注意力机制）如何在临床场景中支持 SSL？
• RQ3 如何将 SSL 方法组织起来以增强生物医学成像任务的实用性和采用率？
• RQ4 加速基于 SSL 的模型在临床部署的实际考虑因素有哪些？

主要发现

• 识别并解释了五类 SSL 技术：解释技巧、弱监督学习、迁移学习、主动学习和杂项方法。
• 强调数据增强、领域知识、传统浅层方法和注意力机制作为有用的辅助工具。
• 强调解释方法对临床决策和模型透明性的重要性。
• 该综述旨在通过汇总和澄清生物医学图像分析的 SSL 策略来加速临床应用。
• 演示在在线 URL 提供以说明概念。