Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image

Zheng, Siting; Li, Xuechen; Bi, Mingmin; Wang, Yuxuan; Liu, Haiyan; Feng, Xiaoshan; Fan, Yunping; Shen, Linlin

doi:10.1109/cbms55023.2022.00074

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…DetCo [60] is a second extension to MoCo that uses many projection heads to improve dense predictions. DenseCL and DetCo have been proven on dense predictions, SimCLR and MoCo in the biomedical environment [59], [60], [63], [67]. Barlow Twins, BYOL, AE were evaluated neither on dense predictions nor biomedical data.…”

Section: Related Workmentioning

confidence: 99%

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Rettenberger

Schilling

Elser

et al. 2023

IEEE Trans. Biomed. Eng.

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The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce such efforts are desired. Methods: Self-Supervised Learning (SSL) is an emerging field that increases performance when unannotated data is present. However, profound studies regarding segmentation tasks and small datasets are still absent. A comprehensive qualitative and quantitative evaluation is conducted, examining SSL's applicability with a focus on biomedical imaging. We consider various metrics and introduce multiple novel application-specific measures. All metrics and state-of-the-art methods are provided in a directly applicable software package (https://osf.io/gu2t8/). Results: We show that SSL can lead to performance improvements of up to 10%, which is especially notable for methods designed for segmentation tasks. Conclusion: SSL is a sensible approach to data-efficient learning, especially for biomedical applications, where generating annotations requires much effort. Additionally, our extensive evaluation pipeline is vital since there are significant differences between the various approaches. Significance: We provide biomedical practitioners with an overview of innovative data-efficient solutions and a novel toolbox for their own application of new approaches. Our pipeline for analyzing SSL methods is provided as a ready-to-use software package.

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Section: Related Workmentioning

confidence: 99%

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Rettenberger

Schilling

Elser

et al. 2023

IEEE Trans. Biomed. Eng.

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Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

Cai,

Xiu,

Song

et al. 2024

NSS

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Purpose This study aims to develop a deep learning methodology for quantitative assessing adenoid hypertrophy in nasopharyngoscopy images and to investigate its correlation with the apnea-hypopnea index (AHI) in pediatric patients with obstructive sleep apnea (OSA). Patients and Methods A total of 1642 nasopharyngoscopy images were collected from pediatric patients aged 3 to 12 years. After excluding images with obscured secretions, incomplete adenoid exposure, 1500 images were retained for analysis. The adenoid-to-nasopharyngeal (A/N) ratio was manually annotated by two experienced otolaryngologists using MATLAB’s imfreehand tool. Inter-annotator agreement was assessed using the Mann–Whitney U -test. Deep learning segmentation models were developed with the MMSegmentation framework, incorporating transfer learning and ensemble learning techniques. Model performance was evaluated using precision, recall, mean intersection over union (MIoU), overall accuracy, Cohen’s Kappa, confusion matrices, and receiver operating characteristic (ROC) curves. The correlation between the A/N ratio and AHI, derived from polysomnography, was analyzed to evaluate clinical relevance. Results Manual evaluation of adenoid hypertrophy by otolaryngologists (p=0.8507) and MATLAB calibration (p=0.679) demonstrated high consistency, with no significant differences. Among the deep learning models, the ensemble learning-based SUMNet outperformed others, achieving the highest precision (0.9616), MIoU (0.8046), overall accuracy (0.9182), and Kappa (0.87). SUMNet also exhibited superior consistency in classifying adenoid sizes. ROC analysis revealed that SUMNet (AUC=0.85) outperformed expert evaluations (AUC=0.74). A strong positive correlation was observed between the A/N ratio and AHI, with the correlation coefficients for SUMNet-derived ratios ranging from r=0.9052 (tonsils size+1) to r=0.4452 (tonsils size+3) and for expert-derived ratios ranging from r=0.4590 (tonsils size+1) to r=0.2681 (tonsils size+3). Conclusion This study introduces a precise and reliable deep learning-based method for quantifying adenoid hypertrophy and addresses the challenge posed limited sample sizes in deep learning applications. The significant correlation between adenoid hypertrophy and AHI underscores the clinical utility of this method in pediatric OSA diagnosis.

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Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image

Cited by 2 publications

References 19 publications

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

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