Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network

Wang, Qi; Qiu, Jiahui; Zhou, Yangming; Ruan, Tong; Gao, Daqi; Gao, Ju

doi:10.1109/bibm.2018.8621136

Cited by 11 publications

(4 citation statements)

References 34 publications

(47 reference statements)

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“…As demonstrated in Table II, the proposed model achieves the best performance, and its precision, recall and F 1 -score reach 96.69%, 97.09% and 96.89%, which outperforms the second method by 0.2%, 0.40% and 1.20%, respectively. To further investigate the effectiveness of the proposed model on RC, we use two RC models in medical domain (i.e., RCN [13] and CNN [22]) and one joint model in generic domain (i.e., Joint-Bi-LSTM [4]) as baseline methods. Since RCN and CNN methods are only applied to RC tasks and cannot extract entities from the text, so we directly use the correct entities in the text to evaluate the RC models.…”

Section: Resultsmentioning

confidence: 99%

“…Neural network methods can extract the relation features without complicated feature engineering. e.g., recurrent capsule network [13] and domain invariant convolutional neural network [14]. However, These methods cannot utilize joint features between entity and relation, resulting in lower generalization performance when compared with joint learning methods.…”

Section: B Relation Classificationmentioning

confidence: 99%

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Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Kui

Zhou

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natural language processing tasks. In this paper, we present a focused attention model for the joint entity and relation extraction task. Our model integrates well-known BERT language model into joint learning through dynamic range attention mechanism, thus improving the feature representation ability of shared parameter layer. Experimental results on coronary angiography texts collected from Shuguang Hospital show that the F1-scores of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which outperform state-of-the-art methods by 1.65% and 1.22%, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: B Relation Classificationmentioning

confidence: 99%

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Kui

Zhou

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Self Cite

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“…These methods have complex computation in data processing. On the other hand, the neural network methods, such as the recurrent capsule network [33] and domain invariant convolution neural network [38], took less computation in extracting features than the conventional methods.Furthermore, in recent years, Deep learning becomes a mainstream method in relation to extraction. In 2014, Zeng et al.…”

Section: Related Workmentioning

confidence: 99%

Semantics analysis model based on deep learning for vessel traffic service application

Xue

Pan

Liu

et al. 2023

IET Intelligent Trans Sys

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Vessel Traffic Service (VTS) significantly improves the navigation efficiency of ports. This paper proposes a model called Joint Extraction of Triples from the VHF Speech (JER‐VHF) to ensure the efficiency of the VTS. Numerous texts are extracted from the Very High Frequency (VHF) speech communication contents and these texts are organized into a dataset named VHFDT. The proposed model's transforming task transforms the voice communication contents of this dataset into a triple representation. VHFDT has a large number of overlapping triples. Therefore, this paper proposes a combined model with three categories to model the entity relations in VHF sentences, including pre‐training Chinese language model for initializing embedding from VHFDT, BiLSTM for rich features, and Multi‐head Attention for focusing on triples. In experimental part, this study uses Precision(P), Recall(R), and F1 to evaluate the accuracy and effectiveness of the proposed method and baseline models. According to experimental results, the proposed model efficiently extracts the key information from complex language environment and achieves better work on relational triple extraction than other baseline models. The model achieved an F1‐score of 83.2% on the overlapping testing data, which is an improvement of 1.8% compared to the second‐best baseline model.

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“…Coronary angiography is the gold standard for computer-aided diagnosis (CAD), so it is essential to describe in detail the position and the degree of stenosistext through coronary angiography for classifing the severity of CAD. Wang et al [17] used recursive capsule network (RCN) to extract the semantic relationship between clinically named entities in the coronary angiography text, so as to automatically find out the maximum stenosis degree of each lumen, and finally inferred the coronary artery severity according to the improved Gensini method. e MURA dataset is the largest public musculoskeletal image dataset available currently.…”

Section: Related Workmentioning

confidence: 99%

A Medical Image Classification Model Based on Adversarial Lesion Enhancement

Zhang

2021

Scientific Programming

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With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learning can assist doctors to a certain extent. However, the latent information in medical images, such as lesion features, is ignored in most of the traditional methods. The extraction of this information is regarded as a learning task within the network in some recent researches, but it requires a large amount of fine-labeled data, which is undoubtedly expensive. In response to the problem above, this paper proposes an Adversarial Lesion Enhancement Neural Network for Medical Image Classification (ALENN), which is used to locate and enhance the lesion information in medical images only under weakly annotated data so as to improve the accuracy of the auxiliary diagnosis model. This method is a two-stage framework, including a structure-based lesion adversarial inpainting module and a lesion enhancement classification module. The first stage is used to repair the lesion area in the images while the second stage is used to locate the lesion area and use the lesion enhanced data during modeling process. In the end, we verified the effectiveness of our method on the MURA dataset, a musculoskeletal X-ray dataset released by Stanford University. Experimental results show that our method can not only locate the lesion area but also improve the effectiveness of the auxiliary diagnosis model.

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Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network

Cited by 11 publications

References 34 publications

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Semantics analysis model based on deep learning for vessel traffic service application

A Medical Image Classification Model Based on Adversarial Lesion Enhancement

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