A novel deep convolution generative adversarial transfer learning model for data-driven assembly quality diagnosis

2021 ◽  
pp. 1-15
Author(s):  
Wentao Luo ◽  
Pingfa Feng ◽  
Jianfu Zhang ◽  
Dingwen Yu ◽  
Zhijun Wu
Keyword(s):  
Transfer Learning ◽  
Feature Learning ◽  
Learning Model ◽  
Small Sample ◽  
Data Driven ◽  
Training Dataset ◽  
Small Data ◽  
Assembly Quality ◽  
Learning Module ◽  
Quality Diagnosis

As the service life of the assembly equipment are short, the tightening data it produces are very limited. Therefore, data-driven assembly quality diagnosis is still a challenge task in industries. Transfer learning can be used to address small data problems. However, transfer learning has strict requirements on the training dataset, which is hard to satisfy. To solve the above problem, an Improved Deep Convolution Generative Adversarial Transfer Learning Model (IDCGAN-TM) is proposed, which integrates three modules: The generative learning module automatically produces source datasets based on small target datasets by using the improved generative-adversarial theory. The feature learning module improves the feature extraction ability by building a lightweight deep learning model (DL). The transfer learning module consists of a pre-trained DL and a one fully connected layer to better perform the intelligent quality diagnosis on the training small sample data. A parallel computing method is adopted to obtain produced source data efficiently. Real assembly quality diagnosis cases are designed and discussed to validate the advance of the proposed model. In addition, the comparison experiments are designed to show that the proposed approach holds the better transfer diagnosis performance compared with the existing three state-of-art approaches.

scholarly journals The Classification Between Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning from Disease-Related Images: A Retrospective Study

2020 ◽  
Author(s):  
Bo Hu ◽  
Lin-Feng Yan ◽  
Yang Yang ◽  
Ying-Zhi Sun ◽  
Cui Yue ◽  
...  
Keyword(s):  
Sample Size ◽  
Transfer Learning ◽  
Small Sample Size ◽  
Transitional Zone ◽  
Small Sample ◽  
Fine Tuning ◽  
Small Data ◽  
Deep Convolutional Neural Networks ◽  
Tuning Method ◽  
Apparent Diffusion

Abstract Background The diagnosis of prostate transition zone cancers (PTZC) remains a clinical challenge due to its similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks showed high efficacy in medical imaging but was limited by the small data size. A transfer learning method was combined with deep learning to overcome this challenge.Methods A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (9 patients). Based on the T2 weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps of these patients, DCNN models were trained and compared between different TL database (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning or transductive transferring).Results PTZC and BPH can be classified through traditional DCNN. The efficacy of transfer learning from ImageNet was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive transfer learning from disease-related images had the comparable efficacies with the fine-tuning method. Limitations include retrospective design and relatively small sample size.Conclusion For PTZC with a small sample size, the accurate diagnosis can be achieved via the deep transfer learning from disease-related images.

scholarly journals Complex Scene Understanding and Recognition Based on the Explainable Machine Learning in AI Models

2021 ◽  
Author(s):  
Bin Wu ◽  
Yuhong Fan ◽  
Yeh-Cheng Chen ◽  
Tao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Network Model ◽  
Scene Understanding ◽  
Learning Model ◽  
Small Sample ◽  
Theoretical Research ◽  
Optimization Efficiency

Abstract Information fusion is an important part of numerous neural network systems and other machine learning models. However, there exist some problems about fusion in scene understanding and recognition of complex environment, such as difficulty in feature extraction, small sample size and interpretability of the model. Deep reinforcement learning can combine the perception ability of deep learning with the decision-making ability of reinforcement learning to learn control strategies directly from high-dimensional original data. However, It faces these challenges, such as low optimization efficiency, poor generality of network model, small labeled samples, explainable decisions for users without a strong background on Artificial Intelligence (AI). Therefore, at the level of application and theoretical research, this paper aims to solve the above problems,the main contributions include: (1)optimize the feature representation methods based on spatial-temporal feature of the behavior characteristics in the scene, deep metric learning between adjacent layers and cross-layer learning theory, and then propose a lightweight reinforcement learning network model to solve these problems of the complexity of the model to be explained, the difficulty of extracting feature and the difficulty of tuning parameter; (2)construct the self-paced learning strategy of the deep reinforcement learning model, introduce transfer learning mechanism in the optimization process, and solve the problem of low optimization efficiency and small labeled samples; (3)design the behavior recognition framework of the multi-perspective deep knowledge transfer learning model, construct a explainable behavior descriptor, and solve the problems of poor network generality and weak 1explanation of network. Our research is of great theoretical and practical significance in the fields of artificial intelligence and public security.

scholarly journals On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation

2020 ◽  
Vol 31 (6) ◽  
pp. 1357-1371 ◽  
Author(s):  
Qiong Chen ◽  
Zimu Zheng ◽  
Chuang Hu ◽  
Dan Wang ◽  
Fangming Liu
Keyword(s):  
Transfer Learning ◽  
Task Allocation ◽  
Learning Model ◽  
Data Driven ◽  
Task Transfer

scholarly journals Errata to “On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation”

2020 ◽  
Vol 31 (11) ◽  
pp. 2569-2569
Author(s):  
Qiong Chen ◽  
Zimu Zheng ◽  
Chuang Hu ◽  
Dan Wang ◽  
Fangming Liu
Keyword(s):  
Transfer Learning ◽  
Task Allocation ◽  
Learning Model ◽  
Data Driven ◽  
Task Transfer

scholarly journals Application Research on the Medical imaging testing of Novel Coronavirus Pneumonia  COVID-19 based on Transfer Learning

2021 ◽  
Author(s):  
shouqiang Liu ◽  
Mingyue Jiang ◽  
Liming Chen ◽  
Yang Wang
Keyword(s):  
Medical Imaging ◽  
Transfer Learning ◽  
Image Data ◽  
Training Model ◽  
Small Sample ◽  
Detection Methods ◽  
Nucleic Acid Detection ◽  
Small Data ◽  
Data Set ◽  
Novel Coronavirus

Abstract Novel coronavirus pneumonia (COVID-19) is a highly infectious and fatal pneumonia-type disease that poses a great threat to the public safety of society. A fast and efficient method for screening COVID19-positive patients is essential. At present, the main detection methods are nucleic acid detection of manual diagnosis and medical imaging (CT image/X-ray image), both of which take a long time to obtain the diagnosis result. This paper discusses the common processing methods for the problem of insufficient medical image data. Then, transfer learning and convolutional neural network were used to construct the screening and diagnosis model of COVID-19, and different migration models were analyzed and compared to select a better pre-training model, which was trained and analyzed under small data sets. Finally, it analyzes and discusses how to train a highly reliable model to quickly help doctors provide advice in the critical moment of epidemic prevention and control when only a small sample data set is available.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Gustaf Halvardsson ◽  
Johanna Peterson ◽  
César Soto-Valero ◽  
Benoit Baudry
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Web Application ◽  
Training Dataset ◽  
Motion Processing ◽  
Image Perception ◽  
Sign Languages ◽  
High Level

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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