We demonstrate that a high learning rate, small network size, and early stopping in unsupervised deep learning for IVIM model fitting can result in sub-optimal solutions and correlated parameters. In simulations, we show that prolonging training beyond early stopping resolves these correlations and reduces parameter error, providing an alternative to exhaustive hyperparameter optimization. However, extensive training results in increased noise sensitivity, tending towards the behavior of least squares fitting. In in-vivo data from glioma patients, fitting residuals were almost identical between approaches, whereas pseudo-diffusion maps varied considerably, demonstrating the difficulty of fitting D* in these regions.
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