Automatic vehicle counting area creation based on vehicle deep learning detection and DBSCAN

Deep learning and high-performance computing have augmented and speed-up the scope of video-based vehicles' massive counting. The automatic vehicle counts result from the detection and tracking of the vehicles in certain areas or Regions of Interest (ROI). In this paper, we propose a technique to create a counting area with different traffic-flow directions based on YOLO and DBSCAN You Only Look Once version five (YOLOv5) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We compare the performance of the method against the manually counted ground truth. The proposed method showed that it is possible to generate the ROIs (counting areas) according to the traffic flow using deep learning techniques with relatively good accuracy (less than 5 % error). These results are promising but we need to explore the limits of this method with more street-view configurations, time and other detection and tracking algorithms, and in an HPC environment.Peer ReviewedPostprint (author's final draft

A quaternion deterministic monogenic CNN layer for contrast invariance

Deep learning (DL) is attracting considerable interest as it currently achieves remarkable performance in many branches of science and technology. However, current DL cannot guarantee capabilities of the mammalian visual systems such as lighting changes. This paper proposes a deterministic entry layer capable of classifying images even with low-contrast conditions. We achieve this through an improved version of the quaternion monogenic wavelets. We have simulated the atmospheric degradation of the CIFAR-10 and the Dogs and Cats datasets to generate realistic contrast degradations of the images. The most important result is that the accuracy gained by using our layer is substantially more robust to illumination changes than nets without such a layer.The authors would like to thank to CONACYT and Barcelona supercomputing Center. Sebastián Salazar-Colores (CVU 477758) would like to thank CONACYT (Consejo Nacional de Ciencia y Tecnología) for the financial support of his PhD studies under Scholarship 285651. Ulises Moya and Ulises Cortés are member of the Sistema Nacional de Investigadores CONACyT.Peer ReviewedPostprint (author's final draft

Detection, counting, and classification of visual ganglia columns of drosophila pupae

Many neurobiologists use the fruit fly (Drosophila) as a model to study neuron interaction and neuron organization and then extrapolate this knowledge to the nature of human neurological disorders. Recently, the fluorescence microscopy images of fruit-fly neurons are commonly used, because of the high contrast. However, the detection of the neurons or cells is compromised by background signals, generating fuzzy boundaries. As a result, it is still common that in many laboratories, the detection, counting, and analysis of this microscope imagery is still a manual task. An automated detection, counting, and morphological analysis of these images can provide faster data processing and easier access to new information. The main objective of this work is to present a semi-automatic detection-counting system and give the main characteristics of images of the visual ganglia columns in Drosophila. We present the semi-automatic detection, count, segmentation and we concluded that it is possible to obtain an accuracy of 75% (with a Kappa statistic of 0.50) in the shape classification. Additionally, we develop python GUI CC Analyzer which can be used by neurobiology laboratories whose research interests are focused on this topic.Peer ReviewedPostprint (published version

Fast single image defogging with robust sky detection

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.This work was supported in part by the Centro en Investigaciones en Óptica (CIO) and the Consejo Nacional de Ciencia y Tecnología (CONACYT), and in part by the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification

Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance to contrast and illumination changes. Some ideas to overcome the illumination and contrast variations usually have to be tuned manually and tend to fail when tested with other types of data degradation. In this context, we present a new bio-inspired entry layer, M6, which detects low-level geometric features (lines, edges, and orientations) which are similar to patterns detected by the V1 visual cortex. This new trainable layer is capable of coping with image classification even with large contrast variations. The explanation for this behavior is the monogenic signal geometry, which represents each pixel value in a 3D space using quaternions, a fact that confers a degree of explainability to the networks. We compare M6 with a conventional convolutional layer (C) and a deterministic quaternion local phase layer (Q9). The experimental setup is designed to evaluate the robustness of our M6 enriched ConvNet model and includes three architectures, four datasets, three types of contrast degradation (including non-uniform haze degradations). The numerical results reveal that the models with M6 are the most robust in front of any kind of contrast variations. This amounts to a significant enhancement of the C models, which usually have reasonably good performance only when the same training and test degradation are used, except for the case of maximum degradation. Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and explain the robustness effect of the M6 feature maps under any kind of contrast degradations.The authors would like to thank to CONACYT and Barcelona supercomputing Center. Sebastián Salazar-Colores (CVU 477758) would like to thank CONACYT (Consejo Nacional de Ciencia y Tecnología) for the financial support of his PhD studies under Scholarship 285651.Peer ReviewedPostprint (published version

Detection, counting, and classification of visual ganglia columns of drosophila pupae

Many neurobiologists use the fruit fly (Drosophila) as a model to study neuron interaction and neuron organization and then extrapolate this knowledge to the nature of human neurological disorders. Recently, the fluorescence microscopy images of fruit-fly neurons are commonly used, because of the high contrast. However, the detection of the neurons or cells is compromised by background signals, generating fuzzy boundaries. As a result, it is still common that in many laboratories, the detection, counting, and analysis of this microscope imagery is still a manual task. An automated detection, counting, and morphological analysis of these images can provide faster data processing and easier access to new information. The main objective of this work is to present a semi-automatic detection-counting system and give the main characteristics of images of the visual ganglia columns in Drosophila. We present the semi-automatic detection, count, segmentation and we concluded that it is possible to obtain an accuracy of 75% (with a Kappa statistic of 0.50) in the shape classification. Additionally, we develop python GUI CC Analyzer which can be used by neurobiology laboratories whose research interests are focused on this topic.Peer Reviewe

Data augmentation for deep learning of non-mydriatic screening retinal fundus images

Fundus image is an effective and low-cost tool to screen for common retinal diseases. At the same time, Deep Learning (DL) algorithms have been shown capable of achieving similar or even better performance accuracies than physicians in certain image classification tasks. One of the key aspects to improve the performance of DL models is to use data augmentation techniques. Data augmentation reduces the impact of overfitting and improves the generalization capacity of the models. However, the most appropriate data augmentation methodology is highly dependant on the nature of the problem. In this work, we propose a data augmentation and image enhancement algorithm for the task of classifying non-mydriatic fundus images of pigmented abnormalities in the macula. For training, fine tuning and data augmentation, we used the Barcelona Supercomputing Centre cluster CTE IBM Power8+ and Marenostrum IV. The parallelization and optimization of the algorithms were performed using Numba, and Python-Multiprocessing, made compatible with the underlying DL framework used for training the model. We propose and trained a specific DL model from scratch. Our main results are an increase in the number of input images up to a factor of, and report the information of quality images for. As a result, our data augmentation approach results in an increase of up to 9% in classification accuracy.Peer ReviewedPostprint (published version

Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma

To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (5) detection of referable glaucomatous optic neuropathy (GON). Five algorithms were designed. Retrospective study from a database of 306,302 images, Optretina's tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy. Main outcome measure was area under the curve (AUC) with accuracy, sensitivity and specificity. Determination of retinal fundus image had AUC of 0.979 with an accuracy of 96% (sensitivity 97.7%, specificity 92.4%). Determination of good quality retinal fundus image had AUC of 0.947, accuracy 91.8% (sensitivity 96.9%, specificity 81.8%). Algorithm for OD/OS had AUC 0.989, accuracy 97.4%. AMD had AUC of 0.936, accuracy 86.3% (sensitivity 90.2% specificity 82.5%), GON had AUC of 0.863, accuracy 80.2% (sensitivity 76.8%, specificity 83.8%). Deep learning algorithms can differentiate a retinal fundus image from other images. Algorithms can evaluate the quality of an image, discriminate between right or left eye and detect the presence of AMD and GON with a high level of accuracy, sensitivity and specificity