  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.
