Fine-Tuning Deep Learning by Crowd Participation.
Fine-Tuning Deep Learning by Crowd Participation.
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DOI：
10.1109/mpul.2018.2866356
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发表时间：
2018-09-01
期刊：
影响因子：
0.6
通讯作者：
Albarqouni, Shadi
中科院分区：
文献类型：
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作者：
Albarqouni, Shadi
目前研究人员在将深度学习模型应用于医学图像分析时面临的主要挑战之一是标注数据的数量有限。收集这种基本事实注释需要领域知识、成本和时间，这使得它对于大型数据库来说是不可行的。Albarqouni等人。[S5]提出了一种新的概念，通过将稳健的聚合层引入卷积神经网络(图S2)，用于从通过众包平台(例如Amazon Machine Turk和CrowdFlowers)收集的噪声注释中学习DL模型(图S2)。他们提出的方法在公开可用的乳腺癌组织学图像数据库上得到了验证，与多数投票的基线相比，他们的稳健聚合方法显示了惊人的结果。在后续工作中，Albarqouni等人。[S6]介绍了生物医学图像从图像到视频游戏对象的转换的新概念。这项技术允许将医学图像表示为星形对象，这些对象可以很容易地嵌入到现成的游戏画布中。该方法降低了标注对领域知识的需求。与传统的众包平台相比，报告的结果令人兴奋和充满希望。
One of the major challenges currently facing researchers in applying deep learning (DL) models to medical image analysis is the limited amount of annotated data. Collecting such ground-truth annotations requires domain knowledge, cost, and time, making it infeasible for large-scale databases. Albarqouni et al. [S5] presented a novel concept for learning DL models from noisy annotations collected through crowdsourcing platforms (e.g., Amazon Mechanical Turk and Crowdflower) by introducing a robust aggregation layer to the convolutional neural networks (Figure S2). Their proposed method was validated on a publicly available database on breast cancer histology images, showing astonishing results of their robust aggregation method compared to the baseline of majority voting. In follow-up work, Albarqouni et al. [S6] introduced the novel concept of a translation from an image to a video game object for biomedical images. This technique allows medical images to be represented as star-shaped objects that can be easily embedded into a readily available game canvas. The proposed method reduces the necessity of domain knowledge for annotations. Exciting and promising results were reported compared to the conventional crowdsourcing platforms.