Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) parametric maps, and the WM and GM probability maps. Results: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.