Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening ...suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.
Video analytics revisited Choudhary, Ayesha; Chaudhury, Santanu
IET computer vision,
06/2016, Volume:
10, Issue:
4
Journal Article
Peer reviewed
Open access
Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover ...patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
Egocentric vision data captures the first person perspective of a visual stimulus and helps study the gaze behavior in more natural contexts. In this work, we propose a new dataset collected in a ...free viewing style with an end-to-end data processing pipeline. A group of 25 participants provided their gaze information wearing Tobii Pro Glasses 2 set up at a museum. The gaze stream is post-processed for handling missing or incoherent information. The corresponding video stream is clipped into 20 videos corresponding to 20 museum exhibits and compensated for user's unwanted head movements. Based on the velocity of directional shifts of the eye, the I-VT algorithm classifies the eye movements into either fixations or saccades. Representative scanpaths are built by generalizing multiple viewers' gazing styles for all exhibits. Therefore, it is a dataset with both the individual gazing styles of many viewers and the generic trend followed by all of them towards a museum exhibit. The application of our dataset is demonstrated for characterizing the inherent gaze dynamics using state trajectory estimator based on ancestor sampling (STEAS) model in solving gaze data classification and retrieval problems. This dataset can also be used for addressing problems like segmentation, summarization using both conventional machine and deep learning approaches.
Recent advances in swarm inspired optimization algorithms have shown its extensive acceptance in solving a wide range of different real-world problems. Particle Swarm Optimization (PSO) is one of the ...most explored nature-inspired population-based stochastic optimization algorithm. In this paper, a Multi-level Particle Swarm Optimization (MPSO) algorithm is proposed to find the architecture and hyperparameters of the Convolutional Neural Network (CNN) simultaneously. This automated learning will reduce the overhead of human experts to find these parameters through manual analysis and experiments. The proposed solution uses multiple swarms at two levels. The initial swarm at level-1 optimizes architecture and multiple swarms at level-2 optimize hyperparameters. The proposed method has used sigmoid like inertia weight to adjust the exploration and exploitation property of particles and avoid the PSO algorithm to prematurely converge into a local optimum solution. In this paper, we have explored an approach to suggest the best well-conditioned CNN architecture and its hyperparameters using MPSO in a specified search space. The complexity and performance of MPSO-CNN will depend on the dimension of the search space. The experimental results on 5 benchmark datasets of MNIST, CIFAR-10, CIFAR-100, Convex Sets, and MDRBI have demonstrated one more effective application of PSO in learning a deep neural architecture.
In this work, we propose two deep learning-based architectures tailored for gas identification and quantification, which automatically tune hyper-parameters of the network for optimal performance. ...The immense success of deep learning in the field of computer vision and natural language processing inspired us to design deep learning-based gas identification and quantification network. The first architecture is proposed for gas quantification, which is based on 1D-CNN. It makes use of raw time-series gas sensor array data and provides the concentration of each gas in a mixture of gases. The second architecture is presented for gas quantification, which is based on a deep belief network combined with drift-aware feature adaptation strategy. The proposed models identify and quantify the gases with improved accuracy despite the presence of sensor drift. Additionally, hyper-parameters of both the networks are automatically tuned for optimal performance. Although several pattern recognition methods related to machine learning, fuzzy logic and hybrid models have been used to identify gas and quantify the gases in the mixture, the performances of these techniques enormously depend on the feature engineering and selection of hyper-parameters. Experimental results show that the proposed methods are an effective technique for identifying gases and quantifying the mixture of gases for e-nose data. We also present that the proposed methods outperforms various other methods and can provide higher identification and quantification accuracy in the pres-ence of sensor drift.
Due to the nonsparse representation, the use of compressed sensing (CS) for physiological signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We present a generalized ...Bayesian CS framework that is capable of handling representations that arise in the spatiotemporal setting. The proposed model utilizes the standard linear Gaussian observation model associated with the hierarchical modeling of data using the matrix-variate Gaussian scale mixture (GSM). It deploys various random and deterministic parameters to incorporate the knowledge of spatial and temporal correlation present in data. By varying distributions over random parameters, a family of generalized hyperbolic matrix variate distributions is derived. For estimation, we rely on variational Bayes (VB) for random parameters and expectation-maximization (EM) for deterministic parameters. Furthermore, the model is compared with recent developments in matrix-variate distribution-based modeling of data, and we briefly discuss its extension to finite mixtures of skewed distributions. Finally, the framework is applied to the steady-state visual evoked potential (SSVEP)-based EEG benchmark data set, and a comparative study is conducted to show its effectiveness for the frequency detection task. One of the crucial features of the proposed model is that it simultaneously processes multichannel signals with low computational cost and time, making it suitable for real-time systems, especially in a resource-constrained environment.
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•A novel relevance score for estimating brain connectivity from fMRI signal using deep neural networks is presented.•Deep neural networks (DNN) have shown state-of-the-art ...performances in neuroscience domain.•However, DNNs have been under critical examination for lack of transparency.•We employ an explainable neural network approach for estimating relevance score of brain connectivity.•The proposed score provides an interpretation of the trained neural network weights with respect to the brain connectivity.
Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach.
We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity.
Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment.
Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation.
The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications.
3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambiguously. One needs to ...plan different views around the given object in order to recognize it. Such a task involves an active sensor—one whose parameters (external and/or internal) can be changed in a purposive manner. In this paper, we review two important applications of an active sensor. We first survey important approaches to active 3-D object recognition. Next, we review existing approaches towards another important application of an active sensor namely, that of scene analysis and interpretation.
Driving behavior analysis benefits the transportation system in terms of road safety, maintenance costs, vehicle’s off-road time, fuel consumption, and enhanced driving experiences. In this paper, we ...present FedSafe, a driver stress and behavior-based recommendation system using federated learning (FL). The proposed system recognizes the drivers’ stressed conditions and driving behavior using vehicle telematics and physiological data to recommend the driver for the next trip, ensuring stress-free and safe driving behavior. Here, federated learning enables collaborative learning from a large amount of data belonging to different vehicles without sharing the raw data among the drivers, ensuring reduced data transmission overhead by allowing computation on the end devices, which helps to meet the increased demand for computing on the edge of the vehicular network. The extensive human-in-the-loop study on three publicly available natural driving datasets (i.e., UAH DriveSet, HCI Lab, PhysioNet) shows that the proposed technique can predict driver stress and identifies the behavior with an accuracy of 97% and 98%, respectively, in terms of AUC and F-measure with approximately 25 times lower transmission overhead using FL.
•Driving can be stressful and needs monitoring to ensure road safety.•Using federated learning, we reduced communication overhead by ≊25 times.•Vehicle Telematics data is also effective in driver behavior and stress analysis.•We identified how a driver acts on a vehicle in particular stress-behavioral condition.
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