Random effects during training: Implications for deep learning-based medical image segmentation-Reference-Cited by-同舟云学术

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Random effects during training: Implications for deep learning-based medical image segmentation

Published:2024-09 Issue: Volume:180 Page:108944
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Åkesson Julius^ORCID,Töger Johannes^ORCID,Heiberg Einar

Publisher

Elsevier BV

Reference25 articles.

1. Deep learning for cardiac image segmentation: a review;Chen;Front. Cardiovasc. Med.,2020

2. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation;Isensee;Nat. Methods,2021

3. On hyperparameter optimization of machine learning algorithms: theory and practice;Yang;Neurocomputing,2020

4. U-net: convolutional networks for biomedical image segmentation;Ronneberger,2015

5. Approximate statistical tests for comparing supervised classification learning algorithms;Dietterich;Neural Comput.,1998

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