Semi-supervised NPC segmentation with uncertainty and attention guided consistency

Hu, Lin; Li, Jiaxin; Peng, Xingchen; Xiao, Jianghong; Zhan, Bo; Zu, Chen; Wu, Xi; Zhou, Jiliu; Wang, Yan

doi:10.1016/j.knosys.2021.108021

Cited by 51 publications

(7 citation statements)

References 41 publications

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“…One issue with the mean-teacher is that the ensemble model may produce unreliable results. Thus, uncertainty estimation [12], [13], [14] is integrated with the mean-teacher network to facilitate more reliable knowledge transfer from the ensemble, resulting in more accurate segmentation labels than competing methods. Uncertainty-guided mean-teacher networks have been further improved by using triple-uncertainty in [20], [21], where data, model and task-level consistencies are combined via multi-task learning to exploit unlabeled data with higher efficiency.…”

Section: Related Work a Semi-supervised Learning For Segmentationmentioning

confidence: 99%

Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

Zhu¹,

Bolsterlee²,

Chow³

et al. 2023

Preprint

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Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information acquired from a single dimensionality (2D/3D), resulting in sub-optimal performance on challenging data, such as magnetic resonance imaging (MRI) scans with multiple objects and highly anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve highly effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities. This hybrid learning mechanism allows HD-Teacher to combine the 'best of both worlds', utilizing features extracted from either 2D, 3D, or both dimensions to produce outputs as it sees fit. Outputs from 2D and 3D teacher models are also dynamically combined, based on their individual uncertainty scores, into a single hybrid prediction, where the hybrid uncertainty is estimated. We then propose a hybrid regularization module to encourage both student models to produce results close to the uncertainty-weighted hybrid prediction. The hybrid uncertainty suppresses unreliable knowledge in the hybrid prediction, leaving only useful information to improve network performance further. Extensive experiments of binary and multi-class segmentation conducted on three MRI datasets demonstrate the effectiveness of the proposed framework. Code is available at https://github.com/ThisGame42/Hybrid-Teacher.

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Section: Related Work a Semi-supervised Learning For Segmentationmentioning

confidence: 99%

Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

Zhu¹,

Bolsterlee²,

Chow³

et al. 2023

Preprint

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“…Chen et al [61] propose multi-level consistency loss which computes the similarities between multi-scale features in an additional discriminator, where the inputs are the segmentation regions by multiplying the unlabeled input image with predicted segmentation probability maps instead of segmentation probability maps. Hu et al [62] propose attention guided consistency which encourages the attention maps from the student model and the teacher model to be consistent. Each image contains the same class object, so different images share similar semantics in the feature space.…”

Section: Unsupervised Regularization With Consistency Learningmentioning

confidence: 99%

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Jiao¹,

Zhang²,

Liu³

et al. 2022

Preprint

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Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.

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“…Another common semi-supervised method is co-training, which uses the interworking between two networks to improve the segmentation performance [49,50]. Hu et al [51] proposed uncertainty and attention guided consistency semi-supervised method to segment nasopharyngeal carcinoma. Lou et al [52] proposed a semi-supervised method that extends the backbone segmentation network to produce pyramidal predictions at different scales.…”

Section: Semi-supervisedmentioning

confidence: 99%

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

Chen

Han

Lin

et al. 2022

Sensors

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Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

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Semi-supervised NPC segmentation with uncertainty and attention guided consistency

Cited by 51 publications

References 41 publications

Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

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