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In the rapidly evolving era of machine learning and deep learning, new algorithms are
constantly emerging, each built upon existing research and pushing the boundaries in
the field of medical imaging. However, one of the major challenges in the application
of these algorithms is the distributional shifts that occur in real-world datasets. This
research paper utilizes the expanded Shifts 2.0 dataset that was released for The Shift
Challenge 2022. It presents how to enhance the UNET model’s robustness and uncertainty estimations in the segmentation of white matter lesions in Multiple Sclerosis
patients, using only the FLAIR modality. This approach examines the impact of multiple hyperparameters on the results of the Shift 2.0 dataset. The suggested model
yielded R-AUC scores of 1.12 and 1.60 on the Dev-out and Eval-out of the shift dataset,
in contrast to the baseline UNET method which registered scores of 4.66 and 7.40 on
those respective partitions. Moreover, the paper establishes that the performance of an
ensemble of UNET models can be comparable to that of a transformer-based ensemble
of UNETR models, offering promising implications for future research and applications.