Semi-supervised Organ Segmentation with Mask Propagation Refinement and Uncertainty Estimation for Data Generation

Pham, Minh-Khoi; Nguyen-Ho, Thang-Long; Dao, Thao Thi Phuong; Nguyen, Tan-Cong; Tran, Minh–Triet

doi:10.1007/978-3-031-23911-3_15

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…We analyzed its definition and its hyperparameters µ and γ. In terms of their definition, we fixed the hyperparameters µ and γ to be 0.5 and 40, respectively, and investigated four functions: sigmoid, cosine, linear, and Equation (8). As shown in Table 5, the effect of using the sigmoid function was obviously better than those of the other functions, and it was more suitable for the teacher model update rules in this experiment.…”

Section: Effect Of the Hyperparametersmentioning

confidence: 99%

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Double Consistency Regularization for Transformer Networks

Wan,

Zhang,

2023

Electronics

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The large-scale and deep-layer deep neural network based on the Transformer model is very powerful in sequence tasks, but it is prone to overfitting for small-scale training data. Moreover, the prediction result of the model with a small disturbance input is significantly lower than that without disturbance. In this work, we propose a double consistency regularization (DOCR) method for the end-to-end model structure, which separately constrains the output of the encoder and decoder during the training process to alleviate the above problems. Specifically, on the basis of the cross-entropy loss function, we build the mean model by integrating the model parameters of the previous rounds and measure the consistency between the models by calculating the KL divergence between the features of the encoder output and the probability distribution of the decoder output of the mean model and the base model so as to impose regularization constraints on the solution space of the model. We conducted extensive experiments on machine translation tasks, and the results show that the BLEU score increased by 2.60 on average, demonstrating the effectiveness of DOCR in improving model performance and its complementary impacts with other regularization techniques.

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Section: Effect Of the Hyperparametersmentioning

confidence: 99%

“…Some academics employ semi-supervised learning to address the issue of less labeled data and add a significant proportion of unlabeled data to the model training process to address these issues [8]. This approach does not require specific labels and is frequently based on the prediction vector produced by the model.…”

Section: Introductionmentioning

confidence: 99%

Double Consistency Regularization for Transformer Networks

Wan,

Zhang,

2023

Electronics

View full text Add to dashboard Cite

show abstract

Semi-supervised Organ Segmentation with Mask Propagation Refinement and Uncertainty Estimation for Data Generation

Cited by 1 publication

References 13 publications

Double Consistency Regularization for Transformer Networks

Double Consistency Regularization for Transformer Networks

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