论文标题
间隔神经网络作为图像重建的不稳定性检测器
Interval Neural Networks as Instability Detectors for Image Reconstructions
论文作者
论文摘要
这项工作研究了利用深度学习模型进行图像重建任务时可能发生的不稳定性的检测。尽管神经网络通常在经验上胜过传统的重建方法，但它们对敏感医学应用的使用仍然存在争议。的确，在最近的一系列作品中，已经证明了深度学习方法容易受到各种类型的不稳定性的影响，例如，由对抗性噪声或分发特征造成的。有人认为，无论潜在的建筑如何，都可以观察到这种现象，并且没有简单的补救措施。基于此洞察力，目前的工作证明了两种用例如何用作不稳定探测器的不确定性定量方法。特别是，最近提出的间隔神经网络在揭示重建的不稳定性方面非常有效。这种能力对于确保安全地利用基于深度学习的方法进行医学图像重建至关重要。
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks. Although neural networks often empirically outperform traditional reconstruction methods, their usage for sensitive medical applications remains controversial. Indeed, in a recent series of works, it has been demonstrated that deep learning approaches are susceptible to various types of instabilities, caused for instance by adversarial noise or out-of-distribution features. It is argued that this phenomenon can be observed regardless of the underlying architecture and that there is no easy remedy. Based on this insight, the present work demonstrates on two use cases how uncertainty quantification methods can be employed as instability detectors. In particular, it is shown that the recently proposed Interval Neural Networks are highly effective in revealing instabilities of reconstructions. Such an ability is crucial to ensure a safe use of deep learning-based methods for medical image reconstruction.
