Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees -- regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain.
翻译:图像到图像回归是一项重要的学习任务,在生物成像中经常使用。 但是,当前的算法一般不提供统计保障, 防止模型的错误和幻觉。 为了解决这个问题, 我们开发了不确定性量化技术, 并严格地为图像到图像回归问题提供统计保障。 特别是, 我们展示了如何在每一像素上得出不确定性间隔, 保证每个像素包含真实值, 并使用用户指定的信任概率。 我们的方法与任何基础机器学习模型( 如神经网络)一起工作, 并赋予它正式的数学保障 -- 不论真实的未知数据分布或模型选择。 此外, 它们很容易执行, 并且计算成本低廉。 我们评估了我们关于三种图像到图像回归任务的程序: 定量阶段显微镜、 加速磁共振成像和 超分辨率传输的Droophila Melanogaster 大脑电子显微镜。