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.

翻译：深度学习（DL）作为定量磁共振成像的参数估计方法正日益流行。目前已有多种相互竞争的实施方案被提出，它们分别基于监督学习和自监督学习。自监督方法（有时被称为无监督方法）大致以自编码器为基础，而监督方法迄今为止均使用真实标注进行训练。这两种学习范式展现出截然不同的优势：值得注意的是，自监督方法较之监督学习方法能够提供更低偏差的参数估计。这一结果有悖直觉——理论上，通过监督标签引入先验知识应能提升精度。本研究表明，监督方法的这一明显局限性源于对真实训练标签的草率选择。通过使用刻意非真实的标签进行训练，我们证实此前仅与自监督方法相关联的低偏差参数估计，可以在监督学习框架中得到复现甚至超越。该方法为构建基于监督学习的统一深度学习参数估计框架奠定了基础，在该框架中，偏差与方差之间的权衡通过精心调整训练标签得以实现。