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Robust and Efficient Medical Imaging with Self-Supervision
[article]
2022
arXiv
pre-print
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical settings different from the training environment. A common mitigation strategy is to develop separate systems for each clinical setting using site-specific data [1]. However, this quickly becomes impractical as medical data is time-consuming to acquire and
arXiv:2205.09723v2
fatcat:u5cthmwpdzckbdjm4eukrcddka