scholarly journals A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation

2021 ◽  
Vol 9 ◽  
Author(s):  
Yu Wang ◽  
Wanjun Zhang
Keyword(s):  
Left Ventricle ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Sequence Information ◽  
Segmentation Result ◽  
Chamber View ◽  
Segmentation Accuracy ◽  
Sequential Image ◽  
Long Short Term Memory ◽  
First Time

The segmentation of the left ventricle (LV) wall in four-chamber view cardiac sequential image is significant for cardiac disease diagnosis and cardiac mechanisms study; however, there is no successful reported work on sequential four-chambered view LV wall segmentation due to the complex four-chamber structure and diversity of wall motion. In this article, we propose a dense recurrent neural network (RNN) algorithm to achieve accurately LV wall segmentation in a four-chamber view MRI time sequence. In the cardiac sequential LV wall process, not only the sequential accuracy but also the accuracy of each image matters. Thus, we propose a dense RNN to provide compensation for the first long short-term memory (LSTM) cells. Two RNNs are combined in this work, the first one aims at providing information for the first image, and the second RNN generates segmentation result. In this way, the proposed dense RNN improves the accuracy of the first frame image. What is more is that, it improves the effectiveness of information flow between LSTM cells. Obtaining more competent information from the former cell, frame-wise segmentation accuracy is greatly improved. Based on the segmentation result, an algorithm is proposed to estimate cardiac state. This is the first time that deals with both cardiac time-sequential LV segmentation problems and, robustly, estimates cardiac state. Rather than segmenting each frame separately, utilizing cardiac sequence information is more stable. The proposed method ensures an Intersection over Union (IoU) of 92.13%, which outperforms other classical deep learning algorithms.

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

2016 ◽  
Vol 4 ◽  
pp. 371-383 ◽  
Author(s):  
Jie Zhou ◽  
Ying Cao ◽  
Xuguang Wang ◽  
Peng Li ◽  
Wei Xu
Keyword(s):  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Attention Model ◽  
Linear Connections ◽  
New Type ◽  
Long Short Term Memory ◽  
Unknown Words ◽  
First Time

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT’14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT’14 English-to-German task.

scholarly journals iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory

2021 ◽  
Vol 12 ◽  
Author(s):  
Kun Niu ◽  
Ximei Luo ◽  
Shumei Zhang ◽  
Zhixia Teng ◽  
Tianjiao Zhang ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Time Series Data ◽  
Short Term Memory ◽  
Critical Issue ◽  
Series Data ◽  
Sequence Information ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Enhancers are regulatory DNA sequences that could be bound by specific proteins named transcription factors (TFs). The interactions between enhancers and TFs regulate specific genes by increasing the target gene expression. Therefore, enhancer identification and classification have been a critical issue in the enhancer field. Unfortunately, so far there has been a lack of suitable methods to identify enhancers. Previous research has mainly focused on the features of the enhancer’s function and interactions, which ignores the sequence information. As we know, the recurrent neural network (RNN) and long short-term memory (LSTM) models are currently the most common methods for processing time series data. LSTM is more suitable than RNN to address the DNA sequence. In this paper, we take the advantages of LSTM to build a method named iEnhancer-EBLSTM to identify enhancers. iEnhancer-ensembles of bidirectional LSTM (EBLSTM) consists of two steps. In the first step, we extract subsequences by sliding a 3-mer window along the DNA sequence as features. Second, EBLSTM model is used to identify enhancers from the candidate input sequences. We use the dataset from the study of Quang H et al. as the benchmarks. The experimental results from the datasets demonstrate the efficiency of our proposed model.

scholarly journals Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Buzhou Tang ◽  
Jianglu Hu ◽  
Xiaolong Wang ◽  
Qingcai Chen
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Medical Information ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Knowledge Bases ◽  
Short Term ◽  
External Knowledge ◽  
Long Short Term Memory ◽  
First Time

Social media in medicine, where patients can express their personal treatment experiences by personal computers and mobile devices, usually contains plenty of useful medical information, such as adverse drug reactions (ADRs); mining this useful medical information from social media has attracted more and more attention from researchers. In this study, we propose a deep neural network (called LSTM-CRF) combining long short-term memory (LSTM) neural networks (a type of recurrent neural networks) and conditional random fields (CRFs) to recognize ADR mentions from social media in medicine and investigate the effects of three factors on ADR mention recognition. The three factors are as follows: (1) representation for continuous and discontinuous ADR mentions: two novel representations, that is, “BIOHD” and “Multilabel,” are compared; (2) subject of posts: each post has a subject (i.e., drug here); and (3) external knowledge bases. Experiments conducted on a benchmark corpus, that is, CADEC, show that LSTM-CRF achieves better F-score than CRF; “Multilabel” is better in representing continuous and discontinuous ADR mentions than “BIOHD”; both subjects of comments and external knowledge bases are individually beneficial to ADR mention recognition. To the best of our knowledge, this is the first time to investigate deep neural networks to mine continuous and discontinuous ADRs from social media.

scholarly journals Automatic Word Spacing of Korean Using Syllable and Morpheme

2021 ◽  
Vol 11 (2) ◽  
pp. 626
Author(s):  
Jeong-Myeong Choi ◽  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Sequence Information ◽  
Morphological Pattern ◽  
Word Level ◽  
Proposed Model ◽  
Correction Problem ◽  
Long Short Term Memory ◽  
N Gram ◽  
Pattern Information

In Korean, spacing is very important to understand the readability and context of sentences. In addition, in the case of natural language processing for Korean, if a sentence with an incorrect spacing is used, the structure of the sentence is changed, which affects performance. In the previous study, spacing errors were corrected using n-gram based statistical methods and morphological analyzers, and recently many studies using deep learning have been conducted. In this study, we try to solve the spacing error correction problem using both the syllable-level and morpheme-level. The proposed model uses a structure that combines the convolutional neural network layer that can learn syllable and morphological pattern information in sentences and the bidirectional long short-term memory layer that can learn forward and backward sequence information. When evaluating the performance of the proposed model, the accuracy was evaluated at the syllable-level, and also precision, recall, and f1 score were evaluated at the word-level. As a result of the experiment, it was confirmed that performance was improved from the previous study.

scholarly journals Prediction of conotoxin type based on long short-term memory network

2021 ◽  
Vol 18 (5) ◽  
pp. 6700-6708
Author(s):  
Feng Wang ◽  
◽  
Shan Chang ◽  
Dashun Wei ◽  
Keyword(s):  
Short Term Memory ◽  
Text Processing ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Short Term ◽  
Test Set ◽  
Term Memory ◽  
Auc Value ◽  
Long Short Term Memory ◽  
Low Efficiency

<abstract> <p>Aiming at the problems of the wet experiment method in identifying the types of conotoxins, such as the complexity, low efficiency and high cost, this study proposes a method that uses the sequence information of the conotoxin peptides combined with long short term memory networks (LSTM) models to predict the Methods of spirotoxin category. This method only needs to take the conotoxin peptide sequence as input, and adopts the character embedding method in text processing to automatically map the sequence to the feature vector representation, and the model extracts features for training and prediction. Experimental results show that the correct index of this method on the test set reaches 0.80, and the AUC value reaches 0.817. For the same test set, the AUC value of the KNN algorithm is 0.641, and the AUC value of the method proposed in this paper is 0.817, indicating that this method can effectively assist in identifying the type of conotoxin.</p> </abstract>

scholarly journals High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM)

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5302 ◽  
Author(s):  
Yuhao Wang ◽  
Qibai Chen ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
High Precision ◽  
Short Term Memory ◽  
Image Sensor ◽  
Sequence Information ◽  
Short Term ◽  
Term Memory ◽  
Dimensional Measurement ◽  
Long Short Term Memory

In modern industries, high precision dimensional measurement plays a pivotal role in product inspection and sub-pixel edge detection is the core algorithm. Traditional interpolation and moment methods have achieved some success. However, those methods still have shortcomings. For example, the accuracy is still insufficient with the resolution limitation of the image sensor. Moreover, prediction results can be affected by image noise. With the recent success of deep learning technology, we propose a sub-pixel edge detection method based on convolution neural network (CNN) and bi-directional long short-term memory (LSTM). First, one-dimensional visual geometry group-16 (VGG-16) is employed to extract edge features. Then, a transformation operation is developed to generate sequence information. Lastly, bi-directional LSTM with fully-connected layers is introduced to output edge positions. Experimental results on our steel plate dataset demonstrate that our method achieves superior accuracy and anti-noise ability than traditional methods.

scholarly journals Quantifying the nativeness of antibody sequences using long short-term memory networks

2019 ◽  
Vol 32 (7) ◽  
pp. 347-354 ◽  
Author(s):  
Andrew M Wollacott ◽  
Chonghua Xue ◽  
Qiuyuan Qin ◽  
June Hua ◽  
Tanggis Bohnuud ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Sequence Information ◽  
Human Antibody ◽  
Short Term ◽  
Term Memory ◽  
Antibody Libraries ◽  
Antibody Sequence ◽  
Long Short Term Memory ◽  
Lstm Network

Abstract Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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