Dataset of fundus images for the diagnosis of ocular toxoplasmosis.

Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis.

Correlation Between Quality Evaluation Metrics and Teeth Detection Results in Panoramic X-Rays Using Deep Learning.

Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professionals. In this work, we applied different image enhancement techniques as a pre-processing step to determine which image features correlate with improvements in teeth detection in panoramic images using deep learning architectures. We contrasted the performance of five object-detection architectures using 300 panoramic images of a public dataset. We evaluated the enhancement in the pre-processing step and the detection performance. Quality and detection metrics were considered, and the cross-correlation between them was computed for every object-detection method contemplated. We observe the dependence of the detection performance with some image enhancement techniques, especially those that introduce less noise and preserve the global contrast of the image.

Distribution level electric current consumption and meteorological data set of the east region of Paraguay.

This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area.

A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images.

In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians. In this work, we propose a novel methodology to quantify the similarity between the decision rules used by a DL model and an ophthalmologist, based on the assumption that doctors are more likely to trust a prediction that was based on decision rules they can understand. Given an eye fundus image with OT, the proposed methodology compares the segmentation mask of OT lesions labeled by an ophthalmologist with the attribution matrix produced by interpretability methods. Furthermore, an open dataset that includes the eye fundus images and the segmentation masks is shared with the community. The proposal was tested on three different DL architectures. The results suggest that complex models tend to perform worse in terms of likelihood to be trusted while achieving better results in sensitivity and specificity.

Dataset from fundus images for the study of diabetic retinopathy.

This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Next, two expert ophthalmologists have classified the dataset. These data can help physicians and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages. The dataset generated will be useful for ophthalmologists and researchers to work on automatic detection algorithms for Diabetic Retinopathy (DR).

Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks.

Ocular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT. It could also help diagnose atypical cases, being particularly useful for ophthalmologists who have less experience. In this work, we propose the use of a deep learning model to perform automatic diagnosis of ocular toxoplasmosis from images of the eye fundus. A pretrained residual neural network is fine-tuned on a dataset of samples collected at the medical center of Hospital de Clínicas in Asunción, Paraguay. With sensitivity and specificity rates equal to 94% and 93%,respectively, the results show that the proposed model is highly promising. In order to replicate the results and advance further in this area of research, an open data set of images of the eye fundus labeled by ophthalmologists is made available.

A multi-objective approach for designing optimized operation sequence on binary image processing.

In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach.

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.

Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform.

Discrete entropy is used to measure the content of an image, where a higher value indicates an image with richer details. Infrared images are capable of revealing important hidden targets. The disadvantage of this type of image is that their low contrast and level of detail are not consistent with human visual perception. These problems can be caused by variations of the environment or by limitations of the cameras that capture the images. In this work we propose a method that improves the details of infrared images, increasing their entropy, preserving their natural appearance, and enhancing contrast. The proposed method extracts multiple features of brightness and darkness from the infrared image. This is done by means of the multiscale top-hat transform. To improve the infrared image, multiple scales are added to the bright areas and multiple areas of darkness are subtracted. The method was tested with 450 infrared thermal images from a public database. Evaluation of the experimental results shows that the proposed method improves the details of the image by increasing entropy, also preserving natural appearance and enhancing the contrast of infrared thermal images.

Servidor de Terminología Médica para el Hospital de Clínicas de Paraguay utilizando Apache Lucene / Medical Terminology Server for the Hospital de Clínicas de Paraguay using Apache Lucene

Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)

Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU)

Servidor de Terminología Médica para el Hospital de Clínicas de Paraguay utilizando Apache Lucene / Medical Terminology Server for the Hospital de Clínicas de Paraguay using Apache Lucene

Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)

Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU).

Structure Optimization for Large Gene Networks Based on Greedy Strategy.

In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.