Continual atlas-based segmentation of prostate MRI. (arXiv:2311.00548v2 [cs.CV] UPDATED)

Continual learning (CL) methods designed for natural image classification
often fail to reach basic quality standards for medical image segmentation.
Atlas-based segmentation, a well-established approach in medical imaging,
incorporates domain knowledge on the region of interest, leading to
semantically coherent predictions. This is especially promising for CL, as it
allows us to leverage structural information and strike an optimal balance
between model rigidity and plasticity over time. When combined with
privacy-preserving prototypes, this process offers the advantages of
rehearsal-based CL …

arxiv atlas basic classification continual domain domain knowledge image imaging knowledge medical medical imaging mri natural predictions quality segmentation standards