2022
DOI: 10.1002/mp.15627
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Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network

Abstract: Purpose Automated retinal vessel segmentation is crucial to the early diagnosis and treatment of ophthalmological diseases. Many deep‐learning‐based methods have shown exceptional success in this task. However, current approaches are still inadequate in challenging vessels (e.g., thin vessels) and rarely focus on the connectivity of vessel segmentation. Methods We propose using an error discrimination network (D) to distinguish whether the vessel pixel predictions of the segmentation network (S) are correct, a… Show more

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Cited by 4 publications
(2 citation statements)
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“…To our knowledge, no previous study has applied unsupervised DL to a small dataset of CT images. Most DL studies involving small numbers of medical images used supervised [22][23][24] , or semi-supervised DL [25][26][27] . These approaches may be used because supervised DL algorithms are expected to identify features that distinguish among data in small datasets; they require definite answers to focus on during the model training.…”
Section: Discussionmentioning
confidence: 99%
“…To our knowledge, no previous study has applied unsupervised DL to a small dataset of CT images. Most DL studies involving small numbers of medical images used supervised [22][23][24] , or semi-supervised DL [25][26][27] . These approaches may be used because supervised DL algorithms are expected to identify features that distinguish among data in small datasets; they require definite answers to focus on during the model training.…”
Section: Discussionmentioning
confidence: 99%
“…To our knowledge, no previous study has applied unsupervised DL to a small dataset of CT images. Most DL studies involving small numbers of medical images used supervised, [19][20][21] or semi-supervised DL. [22][23][24] These approaches may be used because supervised DL algorithms are expected to identify features that distinguish among data in small datasets; they require de nite answers to focus on during the model training.…”
Section: Discussionmentioning
confidence: 99%