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Online Reflective Learning for Robust Medical Image Segmentation
[article]
2022
arXiv
pre-print
Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation robustness. Based on the reflection-on-action conception, our RefSeg firstly drives the deep model to take action to obtain semantic
arXiv:2207.00476v1
fatcat:nv6kyzwbufghbh4tshuoyrbite