Duo-SegNet: Adversarial Dual-Views for Semi-supervised Medical Image Segmentation

Peiris, Himashi; Chen, Zhaolin; Egan, Gary F.; Harandi, Mehrtash

doi:10.1007/978-3-030-87196-3_40

Cited by 32 publications

(24 citation statements)

References 10 publications

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“…Other than consistency learning, some researches use adversarial methods to encourage the segmentation of unlabeled images to be closer to those of the labeled images. These methods always contain a discriminator to distinguish the inputs from labeled annotations or unlabeled predictions [61], [93], [94], [95]. Zhang et al [93] introduce adversarial learning to encourage the segmentation output of unlabeled data to be similar with the annotations of labeled data.…”

Section: Unsupervised Regularization With Adversarial Learningmentioning

confidence: 99%

“…Chen et al [61] add a discriminator following the segmentation network which is used to distinguish between the input signed distance maps from labeled images or unlabeled images. Peiris et al [95] add a critic network into the segmentation architecture which can perform the min-max game through discriminating between prediction masks and the ground truth masks. The experiments show that it could sharpen boundaries in prediction masks.…”

Section: Unsupervised Regularization With Adversarial Learningmentioning

confidence: 99%

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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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Section: Unsupervised Regularization With Adversarial Learningmentioning

confidence: 99%

Section: Unsupervised Regularization With Adversarial 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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“…Among them, the U-Net and U-Net with attention mechanisms are the fully supervised methods. In addition, the Duo-SegNet [66] and TCSM [67] are the semi-supervised methods. In the validation process, we first train the model using the parameters recommended in the corresponding paper and subsequently analyze the performance of each method on the validation set.…”

Section: Comparison With State-of-the-art Modelsmentioning

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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“…Reference [ 18 ], Reference [ 19 ], Reference [ 20 ], SEGNET [ 21 ], and improved SEGNET were utilized as comparison algorithms. Table 2 shows the experimental results.…”

Section: Experimental Verification and Analysismentioning

confidence: 99%

Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores

2022

Computational Intelligence and Neuroscience

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Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and extracting music score information in editable form from image data is currently a challenge. An improved convolutional neural network for musical score recognition is proposed in this paper. Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure. Use additional branch paths to fuse shallow image details, shallow texture features that are beneficial to small objects, and high-level features of global information, enrich the multi-scale semantic information of the model, and alleviate the problem of the lack of multiscale semantic information in the model. Poor recognition performance is caused by semantic information. By comparing the recognition effects of other models, the experimental results show that the proposed musical score recognition model has a higher recognition accuracy and a stronger generalization performance. The improved generalization performance allows the musical score recognition method to be applied to more types of musical score recognition scenarios, and such a recognition model has more practical value.

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Duo-SegNet: Adversarial Dual-Views for Semi-supervised Medical Image Segmentation

Cited by 32 publications

References 10 publications

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

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

Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores

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