Unboxing the black-box of deep learning based reconstruction of undersampled MRIs - Chatterjee, Soumick; Das, Arnab; Khatun, Rupali; Nürnberger, Andreas (CEUR Workshop Proceedings, 2023)
Deep learning has emerged as a very important area of research and has shown immense potential in solving different kinds of problem, including in the medical field. For tasks like undersampled MRI reconstruction - the process of speeding up MRI acquisition with the help of undersampling, deep learning has shown its dominance over the years. But one of the major problems with deep learning is trust: Complex reasonings done by these models appear black-box to the users. Therefore, to build trust and better acceptability, it is important to open up this black-box nature of these models. For classification models, several approaches have been proposed. Nevertheless, for models dealing with inverse problems, like the reconstruction of the undersampled MRIs, it is more challenging as the output of the model has the same number of output pixels as the input, making the interpretability of such models more complex. This research explores different methods to understand the working mechanism of a deep learning model for the task of undersampled MRI reconstruction.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Blackbox; Deep Learning; Explainability; Interpretability; Inverse Problem; MR Image Reconstruction; MRI