A CNN-based vortex identification method

Deng, Liang; Wang, Yueqing; Yang, Liu; Wang, Fang; Li, Sikun; Liu, Jie

doi:10.1007/s12650-018-0523-1

Cited by 56 publications

(24 citation statements)

References 26 publications

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“…This suggests the possibility that a CNN’s performance is better than that of an RNN because the structural vectorization of the sequential sentences is already reflected in the document-level approach. The advantages of CNNs over RNNs—such as LSTM and gated recurrent units—are that they have a smaller number of parameters, thus delivering good computational speed and affording a more efficient setup of convolutional layers to learn local information than is the case with RNNs [ 28 , 29 ]. In addition, the CNN needs to construct an algorithm into a deeply layered architecture for the machine to learn the whole sequence of sentences efficiently, and this increase in layers can lead to vanishing gradients or exploding problems in the CNN algorithm.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Heo

Kim

Choi

et al. 2020

JPM

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Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3–6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the “bag-of-words” model was used to reflect the number of repetitions of text token. The “sent2vec” method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.

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Section: Discussionmentioning

confidence: 99%

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Heo

Kim

Choi

et al. 2020

JPM

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“…Liu et al [34] proposed a CNN-based shock-wave detection method and a novel loss function to optimize the detection results. Deng et al [35] and Wang et al [36] presented a novel vortex identification method based on a CNN. They utilized the instantaneous vorticity deviation (IVD) to label the ground truth vortex area for the proposed CNN-based model.…”

Section: B Flow Data Analysis Using Machine Learningmentioning

confidence: 99%

Deep Regression Network-Assisted Efficient Streamline Generation Method

Lee

Park

2021

IEEE Access

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Streamlining is one of the most frequently utilized visualization methods to analyze the flow structure of computational fluid dynamics (CFD) data. However, it is challenging to find a set of streamlines showing the most prominent flow across the entire flow field due to the heavy computation time required to generate bundles of streamlines. In this paper, we propose an efficient streamline generation method that removes several seed candidates that are predicted as less important using a 3D U-net based regression model. We employ 3D line integral convolution (LIC) volumes that depict the entire flow field for training data of the proposed learning model and evaluate our method using a real-world CFD data set. We find using our model that we can obtain quality of visualization results comparable to that of the ground truth even when more than 90% of the seed candidates are truncated while operating 6.6∼17.1 times faster than the competing method.INDEX TERMS deep regression network, dilated convolutional neural network, flow visualization, line integral convolution, streamline

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“…The CNN-based vortex identification method consists of two parts: the data processing part and the network part, [5] as shown in Fig. 1.…”

Section: Convolution Neural Network For Vortex Identificationmentioning

confidence: 99%

“…The SoftMax neuron is given by: where m represents the number of classes, z j is the output of the last layer, N PRVE represents the number of neurons of previous layer, W k,j and x k are weight and output the kth neuron of the previous layer to the jth neuron of the SoftMax layer. [5] The Network uses back-propagation with the crossentropy cost function to optimize the parameters. The cross-entropy cost function is shown in Eq.…”

Section: Structure Of the Networkmentioning

confidence: 99%

Analysis of vortex ring formation in the heart chamber by instantaneous vortex deviation based on convolutional neural network

Wong

2021

E3S Web Conf.

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The formation of vortex rings during the left ventricle (LV) filling is an optimized mechanism for blood transport, and the vorticity is an important measure of a healthy heart and LV. There is a relationship between abnormal diastolic vortex structure and impaired LV, and hence vortex identification is vital for understanding the underlying physical mechanism of blood flow. However, due to lack of quantitative methods, defining, computing and mapping the left ventricular vortices has not been rigorously studied previously. In this paper, a novel method of vortex detection based on the convolutional neural network (CNN) is created, which enables determination of the boundary of vortex and integrates the local and global flow fields. We have used the CNN-based vortex identification and vector flow mapping (VFM) to quantify left ventricular vorticity. In the clinical application of our methodology to healthy subjects and uremic patients, we find differences in the strength and position of the vortices between healthy and patients with uremia cardiomyopathy. Our results can accurately indicate the role of vortex formation in intracardiac flow, and provide new insights into the blood flow within the heart structure.

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A CNN-based vortex identification method

Cited by 56 publications

References 26 publications

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Deep Regression Network-Assisted Efficient Streamline Generation Method

Analysis of vortex ring formation in the heart chamber by instantaneous vortex deviation based on convolutional neural network

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