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Medical Image Analysis
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Loss odyssey in medical image segmentation

descriptionPublicationkeyboard_double_arrow_right Article 01 Jul 2021 English Publisher:Elsevier BVJournal:Medical Image Analysis, volume 71, page 102,035 (issn: 1361-8415, Copyright policy )
Authors: Jun Ma 0016; Jianan Chen 0001; Matthew Ng; Rui Huang; Yu Li 0031; Chen Li; Xiaoping Yang 0001; +1 Authors

doi: 10.1016/j.media.2021.102035 , 10.5281/zenodo.4738480 , 10.5281/zenodo.4738479

pmid: 33813286

Loss odyssey in medical image segmentation

Abstract

New segmentation results (based on nnUNetV2) of different loss functions in "Loss Odyssey in Medical Image Segmentation".

Related Organizations
Keywords

Segmentation, Loss Functions, Image Processing, Computer-Assisted, Humans, Neural Networks, Computer, Loss function

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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