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Lifelong nnU-Net: a framework for standardized medical continual learning.
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- معلومة اضافية
- المصدر:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
- بيانات النشر:
Original Publication: London : Nature Publishing Group, copyright 2011-
- الموضوع:
- نبذة مختصرة :
As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net-widely regarded as the best-performing segmenter for multiple medical applications-and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark.
(© 2023. The Author(s).)
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- الموضوع:
Date Created: 20230609 Date Completed: 20230612 Latest Revision: 20231121
- الموضوع:
20240628
- الرقم المعرف:
PMC10256748
- الرقم المعرف:
10.1038/s41598-023-34484-2
- الرقم المعرف:
37296233
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