论文标题
培训标签问题的选择：如何最好地使用深度学习进行定量MRI参数估计
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation
论文作者
论文摘要
深度学习（DL）作为定量MRI的参数估计方法越来越受欢迎。已经提出了一系列竞争实施，依靠监督或自我监督的学习。自我监督的方法有时被称为无监督，是基于自动编码器的松散方法，而迄今为止，受监督的方法已经接受了地面图标签的培训。这两个学习范式已被证明具有不同的优势。值得注意的是，比其监督替代方案提供了低偏置参数估计值。该结果是违反直觉的 - 从理论上讲，将先验知识与监督标签纳入应提高准确性。在这项工作中，我们表明，这种监督方法的明显局限性源于地面训练标签的天真选择。通过对故意不是地面图的标签进行培训，我们表明，在有监督的学习框架内可以复制（并改善）先前与自我监督方法相关的低偏差参数估计。这种方法为基于监督学习的单个，统一的，深度学习参数估计框架奠定了基础，在这种学习中，通过仔细调整培训标签，在偏见和差异之间进行权衡。
Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervised or self-supervised learning. Self-supervised approaches, sometimes referred to as unsupervised, have been loosely based on auto-encoders, whereas supervised methods have, to date, been trained on groundtruth labels. These two learning paradigms have been shown to have distinct strengths. Notably, self-supervised approaches have offered lower-bias parameter estimates than their supervised alternatives. This result is counterintuitive - incorporating prior knowledge with supervised labels should, in theory, lead to improved accuracy. In this work, we show that this apparent limitation of supervised approaches stems from the naive choice of groundtruth training labels. By training on labels which are deliberately not groundtruth, we show that the low-bias parameter estimation previously associated with self-supervised methods can be replicated - and improved on - within a supervised learning framework. This approach sets the stage for a single, unifying, deep learning parameter estimation framework, based on supervised learning, where trade-offs between bias and variance are made by careful adjustment of training label.
