Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images-2019
Abstract Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25e26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05). Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p Z 0.016) superior in classifying the cropped images. Interpretation: With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
KEYWORDS : Melanoma | Pathology | Histopathology | Deep learning | Artificial intelligence
Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways
یادگیری عمیق یکپارچه و مدل تعقیب خودرو تصادفی برای پویایی ترافیک در بزرگراه های چند خطه-2019
The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lanechanging (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lanechangers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone.
Keywords: Stochastic car-following model | Deep learning | Lane-changing behaviour
Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images
تشخیص خودکار ، بومی سازی و تقسیم نانو ذرات با یادگیری عمیق در تصاویر میکروسکوپی-2019
With the growing amount of high resolution microscopy images automatic nano-particle detection, shape analysis and size determination have gained importance for providing quantitative support that gives important information for the evaluation of the material. In this paper, we present a new method for detection of nanoparticles and determination of their shapes and sizes simultaneously with deep learning. The proposed method employs multiple output convolutional neural networks (MO-CNN) and has two outputs: first is the detection output that gives the locations of the particles and the other one is the segmentation output for providing the boundaries of the nano-particles. The final sizes of particles are determined with the modified Hough algorithm that runs on the segmentation output. The proposed method is tested and evaluated on a dataset containing 17 TEM images of Fe3O4 and silica coated nano-particles. Also, we compared these results with U-net algorithm which is a popular deep learning method. The experiments showed that the proposed method has 98.23% accuracy for detection and 96.59% accuracy for segmentation of nano-particles.
Keywords: Nano-particle | Deep learning | Object detection | MO-CNN | Hough transform
Research on image steganography analysis based on deep learning
تحقیق در مورد تجزیه و تحلیل استگانوگرافی تصویر بر اساس یادگیری عمیق-2019
Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges. Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the representative deep learning method CNN, the model is designed and adjusted according to the characteristics of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics such as neighborhood correlation. 2. A steganalysis feature learning method based on global information constraints is proposed. Based on the previous research of steganalysis method based on CNN, this work focuses on the importance of global information in steganalysis feature expression. 3. A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct steganalysis detectors without distinguishing specific types of steganalysis algorithms
Keywords: Steganalysis | Steganography | Feature learning | Deep learning | Convolutional neural network | Transfer learning | Multitask learning
Image anomaly detection for IoT equipment based on deep learning
تشخیص ناهنجاری تصویر برای تجهیزات اینترنت اشیا بر اساس یادگیری عمیق-2019
Intelligent power grid systems is the trend of power development, since traditional methods of manually monitoring power equipment have been unable to meet the requirements of power systems. When an abnormal situation occurs in the operating environment, most monitoring devices cannot be quickly and accurately identified, which may have serious consequences. Aiming at the above problems, in this paper, we propose an anomaly detection algorithm for the monitoring environment of power IoT equipment operating environment based on deep learning from the perspective of personnel identification and fire smoke detection. The multi-stream CNN-based remote monitoring image personnel detection method and the deep convolutional neural network-based fire smoke detection method have achieved good results in personnel identification and fire smoke detection in the power equipment operating environment monitoring image, respectively. This provides a reference for monitoring image anomaly detection.
Keywords: Operating environment monitoring | Image anomaly detection | Deep learning
Road surface condition classification using deep learning
طبقه بندی وضعیت سطح جاده با استفاده از یادگیری عمیق-2019
Traditional image recognition technology currently cannot achieve the fast real-time high-accuracy performance necessary for road recognition in intelligent driving. Deep learning models have been recently emerging as promising tools to achieve this performance. The recognition performance of such models can be boosted using appropriate selection of the activation functions. This paper proposes a deep learning approach for the classification of road surface conditions, and constructs a new activation function based on the rectified linear unit Rectified Linear Units (ReLu) activation function. The experimental results show a classification accuracy of 94.89% on the road state database. Experiments on public datasets demonstrate that the proposed convolutional neural network model with the improved activation function has better generalization and excellent classification performance.
Keywords: Deep learning | Road condition | Activation function | Image recognition | Intelligent driving
Image quality recognition technology based on deep learning
فن آوری تشخیص کیفیت تصویر مبتنی بر یادگیری عمیق-2019
Image plays an important role in today’s society and is an important information carrier. However, due to the problems in shooting or processing, image quality is often difficult to be guaranteed, and low-quality images are often difficult to identify, which results in the waste of information. How to effectively identify low-quality images has become a hot research topic in today’s society. Deep learning has a good application in image recognition. In this paper, it is applied to low-quality image recognition. An image quality recognition technology based on deep learning is studied to effectively realize low-quality image recognition. Firstly, in the stage of image preprocessing, a low-quality image enhancement method is proposed, which uses non-linear transformation to enhance image contrast image, restore image details and enhance image quality. Secondly, the convolutional neural network is used to extract image features, and the L2 regularization method is introduced to optimize the over-fitting problem. Finally, SVM is used to recognize the output of convolutional neural network to realize low quality image recognition. Through simulation analysis, it is found that the image enhancement method proposed in the preprocessing stage can effectively enhance the image quality, and deep learning can effectively realize the recognition of the enhanced image and improve the recognition accuracy.
Keywords: Low quality image | Deep learning | Image recognition | Support vector machines(SVM)
Development of accurate human head models for personalized electromagnetic dosimetry using deep learning
توسعه مدل های دقیق سر انسان برای دوزیمتری الکترومغناطیسی شخصی با استفاده از یادگیری عمیق-2019
The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly generated via the segmentation of magnetic resonance images into different anatomical tissues. This process is time consuming and requires special experience for segmenting a relatively large number of tissues. Thus, it is challenging to accurately compute the electric field in different specific brain regions. Recently, deep learning has been applied for the segmentation of the human brain. However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry. In this study, we propose a new architecture for a convolutional neural network, named ForkNet, to perform the segmentation of whole human head structures, which is essential for evaluating the electrical field distribution in the brain. The proposed network can be used to generate personalized head models and applied for the evaluation of the electric field in the brain during transcranial magnetic stimulation. Our computational results indicate that the head models generated using the proposed network exhibit strong matching with those created via manual segmentation in an intra-scanner segmentation task.
Keywords: convolutional neural network | Deep learning | Image segmentation | Transcranial magnetic stimulation
Improving Workflow Efficiency for Mammography Using Machine Learning
بهبود بهره وری گردش کار برای ماموگرافی با استفاده از یادگیری ماشین-2019
Objective: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. Methods: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient’s nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. Results: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. Conclusion: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy.
Key Words: Breast cancer | deep learning | machine learning | mammography | radiology
A survey on deep learning based face recognition
مروری بر شناخت چهره مبتنی بر یادگیری عمیق-2019
Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed. This paper summarizes about 330 contributions in this area. It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching. A summary of databases used for deep face recognition is given as well. Finally, some open challenges and directions are discussed for future research.
Keywords: Deep learning | Face recognition | Artificial Neural Network | Convolutional Neural Networks | Autoencoder | Generative Adversarial Networks