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

机器学习算法在医学成像中越来越受欢迎，其中高度功能化的 已经训练了算法来识别图像数据集中的模式或特征 相关任务，如肿瘤分割和疾病诊断。近年来，一种被称为 深度学习已经彻底改变了机器学习领域，通过利用大量的数据集和巨大的 从数据中提取特征的计算能力。深度学习非常适合医学成像中的问题， 并且在诸如分割心脏结构、肿瘤和组织的各种任务中获得了成功。 然而，机器学习的研究也表明，深度学习是脆弱的， 对图像的设计扰动可能导致算法失败。这些扰动可以被设计为 人类无法察觉，因此训练有素的放射科医生不会犯同样的错误。深度学习 方法获得认可并走向临床实施，因此开发一种 更好地理解神经网络的性能。具体来说，关键是要了解 当呈现有噪声或不完美的数据时进行深度学习。这个项目的目标是探索这些 问题的背景下，医学成像，以更好地确定优势，弱点，和故障点， 深度学习算法 我们认为，在理论机器学习中研究的恶意扰动类型可能不会被 代表医学图像中遇到的噪声种类。虽然噪声在物理学中是不可避免的， 系统中，由诸如受试者运动、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 对深度学习算法性能的扰动。此外，我们还将研究随机 引入数据集的标记错误，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类型更具鲁棒性的方法 上面描述 总之，尽管神经网络对输入中的小误差的敏感性在神经网络中被广泛认可， 深度学习社区，我们的工作将在特定情况下调查这些普遍现象， 医学成像任务，并进行第一次研究的平均情况下，可能实际出现的错误， 临床研究。此外，我们将提出新的建议，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.