Radiological images and machine learning: Trends, perspectives, and prospects
تصاویر رادیولوژی و یادگیری ماشین: روند، دیدگاه ها، و چشم انداز-2019
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
Keywords: Deep learning | Machine learning | Imaging modalities | Deep neural networ
Transfer learning of deep neural network representations for fMRI decoding
انتقال یادگیری بازنمایی های شبکه عصبی عمیق برای رمزگشایی fMRI-2019
Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view.
Keywords: Deep learning | Convolutional Neural Network | Transfer learning | Brain decoding | fMRI | MultiVoxel Pattern Analysis
A novel deep multi-criteria collaborative filtering model for recommendation system
یک مدل جدید عمیق مشترک چند منظوره برای سیستم توصیه-2019
Recommender systems have been in existence everywhere with most of them using single ratings in prediction. However, multi-criteria predictions have been proved to be more accurate. Recommender systems have many techniques; collaborative filtering is one of the most commonly used. Deep learning has achieved impressive results in many domains such as text, voice, and computer vision. Lately, deep learning for recommender systems began to gain massive interest, and many recommendation models based on deep learning have been proposed. However, as far as we know, there is not yet any study which gathers multi-criteria recommendation and collaborative filtering with deep learning. In this work, we propose a novel multi-criteria collaborative filtering model based on deep learning. Our model contains two parts: in the first part, the model obtains the users and items’ features and uses them as an input to the criteria ratings deep neural network, which predicts the criteria ratings. Those criteria ratings constitute the input to the second part, which is the overall rating deep neural network and is used to predict the overall rating. Experiments on a realworld dataset demonstrate that our proposed model outperformed the other state-of-the-art methods, and this provides evidence pointing to the success of employing deep learning and multi-criteria in recommendation systems
Keywords: Collaborative filtering | Deep learning | Deep neural network | Multi-criteria | Recommender system
A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization
یک روش مبتنی بر یادگیری عمیق برای طراحی مواد محاسباتی نانوساختارهای فلکسوالکتریک با بهینه سازی توپولوژی-2019
We present a deep learning method to investigate the effect of flexoelectricity in nanostructures. For this purpose, deep neural network (DNN) algorithm is employed to map the relation between the inputs and the material response of interest. The DNN model is trained and tested making use of database that has been established by solving the governing equations of flexoelectricity using a NURBS-based IGA formulation at design points in the full probability space of the input parameters. Firstly, pure flexoelectric cantilever nanobeam is investigated under mechanical and electrical loading conditions. Then, structures of composite system constituted by two nonpiezoelectric material phases are addressed in order to find the optimized topology with respect to the energy conversion factor. The results show promising capabilities of the proposed method, in terms of accuracy and computational efficiency. The deep learning method we used have produced superior optimal designs compared to the numerical methods. The findings of this study will be of profound interest to researcher involved further in the optimization and design of flexoelectric structures.
Keywords: Flexoelectricity | Piezoelectricity | Isogeometric analysis (IGA) | Machine learning | Deep neural network | Topology optimization
DNNRec: A novel deep learning based hybrid recommender system
DNNRec: یک سیستم توصیه گر ترکیبی مبتنی بر یادگیری عمیق-2019
We propose a novel deep learning hybrid recommender system to address the gaps in Collaborative Fil- tering systems and achieve the state-of-the-art predictive accuracy using deep learning. While collabo- rative filtering systems are popular with many state-of-the-art achievements in recommender systems, they suffer from the cold start problem, when there is no history about the users and items. Further, the latent factors learned by these methods are linear in nature. To address these gaps, we describe a novel hybrid recommender system using deep learning. The solution uses embeddings for representing users and items to learn non-linear latent factors. The solution alleviates the cold start problem by inte- grating side information about users and items into a very deep neural network. The proposed solution uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy. The proposed solution is benchmarked against existing meth- ods on both predictive accuracy and running time. Predictive Accuracy is measured by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared. Running time is measured by the mean and standard deviation across seven runs. Comprehensive experiments are con- ducted on several datasets such as the MovieLens 100 K, FilmTrust, Book-Crossing and MovieLens 1 M. The results show that the proposed technique outperforms existing methods in both non-cold start and cold start cases. The proposed solution framework is generic from the outperformance on four different datasets and can be leveraged for other ratings prediction datasets in recommender systems.
Keywords: Deep learning | Recommender systems | Embeddings | Side information | Cyclical learning rates | Deep neural network | Cold start problem
Deep-learning-based fault detection and diagnosis of air-handling units
تشخیص خطای مبتنی بر یادگیری عمیق و تشخیص واحدهای انتقال هوا-2019
This study proposed a real-time fault diagnostic model for air-handling units (AHUs); the model used deep learning to improve the operational efficiency of AHUs and thereby reduce the energy consumption of HVAC—heating, ventilating, and air conditioning—systems in buildings. Additionally, EnergyPlus simulation software was employed to establish different types of fault operation behavior data to serve as references for deep learning, thus reducing the complexity of data preprocessing, retaining data completeness, and improving the reliability of the diagnostic model. The proposed deep neural network fault diagnostic model can serve as a reference for this research field; the model features five hidden layers, each comprising 200 neurons. Additionally, this study tested abnormal faults commonly observed in AHUs, including failure to control two-way hydronic valves and variable air volume box dampers as well as supply air temperature sensors exhibiting measurement error. After performing diagnosis with data that had not been used in the training or verification process, the diagnostic results indicated that the diagnostic model exhibited 95.16% accuracy.
Keywords: Deep learning | Deep neural network | Fault detection and diagnosis
Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia
Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia-2019
Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachy- cardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle differ- ent tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved.
Keywords: Physiological signal classification | Heart disease | Neuroevolution | Probabilistic grammar | Genetic programming | Deep neural network
Learning deep neural networks for node classification
یادگیری شبکه های عصبی عمیق برای طبقه بندی گره-2019
Deep Neural Network (DNN) has made great leaps in image classification and speech recognition in re- cent years. However, employing DNN for node classification such as in social network remains to be a non-trivial problem. Moreover, the current advanced method of implementing node classification tasks usually takes two steps, i.e. firstly, the embedding vector of the node is obtained through network em- bedding and then the classifier such as SVM is leveraged to do the task. Distinctly, this may only get the suboptimal solution of the problem. To settle the above issues, a novel Deep Neural Network method for node classification named DNNNC is proposed in the framework of Deep Learning. Specifically, we first get the positive pointwise mutual information (PPMI) matrix from the given adjacency matrix. Then, the data is fed to deep neural network composed of deep stacked sparse autoencoders and softmax layer, which could learn the node representation while encoding the rich nonlinear structural and semantic in- formation and could be well trained for node classification under the DNN framework. Extensive experi- ments are conducted on real-world network datasets for node classification task and have shown that the proposed model DNNNC outperforms the state-of-the-art method in the view of superior performance.
Keywords: Network embedding | Node classification | Deep neural network | Deep learning