2021
DOI: 10.1007/978-3-030-87602-9_9
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False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-supervised Learning

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Cited by 4 publications
(2 citation statements)
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“…The experimental results demonstrated that the proposed CLCR-STNet effectively exerted the potential of unlabeled data and outperformed the supervised methods counterpart. In [107], Du et al devised a semi-supervised detection network to reduce the false positive rate in cervical cytology screening. To be specific, a Reti-naNet was first employed to find the suspicious abnormalities and then a false positive suppression network based on Mean Teacher (MT) model was utilized to execute the further fine-grained classification and decrease the false positive samples.…”
Section: Semi-supervised Learning Based Detectionmentioning
confidence: 99%
See 1 more Smart Citation
“…The experimental results demonstrated that the proposed CLCR-STNet effectively exerted the potential of unlabeled data and outperformed the supervised methods counterpart. In [107], Du et al devised a semi-supervised detection network to reduce the false positive rate in cervical cytology screening. To be specific, a Reti-naNet was first employed to find the suspicious abnormalities and then a false positive suppression network based on Mean Teacher (MT) model was utilized to execute the further fine-grained classification and decrease the false positive samples.…”
Section: Semi-supervised Learning Based Detectionmentioning
confidence: 99%
“…Hu et al utilized semi-supervised contrastive Learning to segment MRI and CT images [172]. For cervical cytology, several semi-supervised learning based methods have also been proposed to detect abnormal cell detection or segment overlapping cells [106][107][108]133]. These approaches have successfully improved the labeling efficiency and exhibit high accuracies which are comparable with full-supervised methods.…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%