Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks
Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks
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DOI：
10.1016/j.visinf.2021.10.001
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发表时间：
2021-10-22
影响因子：
3
通讯作者：
Shamir, Lior
中科院分区：
文献类型：
--
作者：
Dhar, Sanchari;Shamir, Lior
在过去的十年中，深度神经网络，特别是卷积神经网络（CNN），已经成为生物医学图像分析领域的主要工具，并被广泛用于其他领域，如对象或面部识别。CNN在提供上级性能的能力方面具有明显的优势，但不需要完全理解反映手头生物医学问题的图像元素，也不需要为该任务设计特定的算法。易于使用的库的可用性及其非参数性质使CNN成为需要自动生物医学图像分析的问题的最常见解决方案。但是，虽然CNN有许多优点，但它们也有某些缺点。由CNN确定的特征是复杂和不直观的，因此CNN通常作为“黑匣子”工作。此外，CNN从像素数据中可以提供区分信号的任何信息中学习，这使得控制CNN实际学习的内容变得更加困难。在这里，我们遵循常见的做法来测试CNN是否可以对生物医学图像数据集进行分类，但我们不是使用整个图像，而是只使用不具有生物医学内容的图像的一部分。实验表明，即使使用不包含任何生物医学信息的数据集进行训练，或者可能被图像数据中的不相关信息系统性地偏置，CNN也可以提供高的分类精度。这种一致的不相关数据的存在很难识别，因此可能导致有偏见的实验结果。CNN这种缺点的可能解决方案可以是控制实验，以及其他保护性实践，以验证结果并避免基于CNN生成的注释得出有偏见的结论。(C)2021作者（S）由Elsevier B.V.代表浙江大学和浙江大学出版社出版。
In the past decade, deep neural networks, and specifically convolutional neural networks (CNNs), have been becoming a primary tool in the field of biomedical image analysis, and are used intensively in other fields such as object or face recognition. CNNs have a clear advantage in their ability to provide superior performance, yet without the requirement to fully understand the image elements that reflect the biomedical problem at hand, and without designing specific algorithms for that task. The availability of easy-to-use libraries and their non-parametric nature make CNN the most common solution to problems that require automatic biomedical image analysis. But while CNNs have many advantages, they also have certain downsides. The features determined by CNNs are complex and unintuitive, and therefore CNNs often work as a ``Black Box''. Additionally, CNNs learn from any piece of information in the pixel data that can provide a discriminative signal, making it more difficult to control what the CNN actually learns. Here we follow common practices to test whether CNNs can classify biomedical image datasets, but instead of using the entire image we use merely parts of the images that do not have biomedical content. The experiments show that CNNs can provide high classification accuracy even when they are trained with datasets that do not contain any biomedical information, or can be systematically biased by irrelevant information in the image data. The presence of such consistent irrelevant data is difficult to identify, and can therefore lead to biased experimental results. Possible solutions to this downside of CNNs can be control experiments, as well as other protective practices to validate the results and avoid biased conclusions based on CNN-generated annotations. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd.