Can machines be trusted? Robustification of deep learning for medical imaging

机器学习算法在医学成像中变得越来越流行，在医学成像中，机器学习算法具有很高的功能 算法已经被训练成识别图像数据集中的模式或特征，并在临床上执行 相关任务，如肿瘤分割和疾病诊断。近年来，一种被称为 深度学习通过利用海量数据集和海量数据，彻底改变了机器学习领域 从数据中提取特征的计算能力。深度学习非常适合解决医学成像中的问题， 并在心脏结构、肿瘤和组织分割等各种任务中取得了成功。 然而，机器学习方面的研究也表明，深度学习在仔细的意义上是脆弱的 对图像进行有意的扰动可能会导致算法失败。这些扰动可以被设计成 人类无法察觉，这样训练有素的放射科医生就不会犯同样的错误。作为深度学习 方法获得接受并走向临床实施，因此开发一种 更好地了解神经网络的性能。具体地说，了解 当出现噪声或不完美的数据时，深度学习。这个项目的目标是探索这些 医学成像环境中的问题-更好地识别以下方面的优势、弱点和故障点 深度学习算法。 我们假设，在理论机器学习中研究的那种恶意扰动可能不是 代表了在医学图像中遇到的那种噪声。尽管噪音在物理环境中是不可避免的 系统中，由主体运动、操作员错误或仪器故障等来源引起的噪声可能 对深度学习算法的有害影响较小。我们建议描述这些影响的特征 对深度学习算法性能的扰动。此外，我们将研究随机性的影响 引入数据集中的标记错误，这可能是由于诚实的人为错误引起的。我们还将开发新的 使深度学习算法对临床相关扰动的类型更稳健的方法 如上所述。 总而言之，尽管神经网络对输入中的小误差的敏感性在 在深度学习社区，我们的工作将在特定的情况下调查这些普遍现象 医学成像任务，并对实际可能出现的平均病例误差进行第一次研究 临床研究。此外，我们将就如何量化和改善 深度学习方法在医学成像中的弹性。

Machine learning algorithms have become increasing popular in medical imaging, where highly functional algorithms have been trained to recognize patterns or features within image data sets and perform clinically relevant tasks such as tumor segmentation and disease diagnosis. In recent years, an approach known as deep learning has revolutionized the field of machine learning, by leveraging massive datasets and immense computing power to extract features from data. Deep learning is ideally suited for problems in medical imaging, and has enjoyed success in diverse tasks such as segmenting cardiac structures, tumors, and tissues. However, research in machine learning has also shown that deep learning is fragile in the sense that carefully designed perturbations to an image can cause the algorithm to fail. These perturbations can be designed to be imperceptible by humans, so that a trained radiologist would not make the same mistakes. As deep learning approaches gain acceptance and move toward clinical implementation, it is therefore crucial to develop a better understanding of the performance of neural networks. Specifically, it is critical to understand the limits of deep learning when presented with noisy or imperfect data. The goal of this project is to explore these questions in the context of medical imaging—to better identify strengths, weaknesses, and failure points of deep learning algorithms. We posit that malicious perturbations, of the type studied in theoretical machine learning, may not be representative of the sort of noise encountered in medical images. Although noise is inevitable in a physical system, the noise arising from sources such as subject motion, operator error, or instrument malfunction may have less deleterious effects on a deep learning algorithm. We propose to characterize the effect of these perturbations on the performance of deep learning algorithms. Furthermore, we will study the effect of random labeling error introduced into the data set, as might arise due to honest human error. We will also develop new methods for making deep learning algorithms more robust to the types of clinically relevant perturbations described above. In summary, although the susceptibility of neural networks to small errors in the inputs is widely recognized in the deep learning community, our work will investigate these general phenomena in the specific case of medical imaging tasks, and also conduct the first study of average-case errors that could realistically arise in clinical studies. Furthermore, we will produce novel recommendations for how to quantify and improve the resiliency of deep learning approaches in medical imaging.