TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation

Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an ef cient and fast segmentation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed.With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google's publicly available colab environment, a generalized fully con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-

A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends

Currently available reviews in the area of artificial intelligence-based music generation do not provide a wide range of publications and are usually centered around comparing very specific topics between a very limited range of solutions. Best surveys available in the field are bibliography sections of some papers and books which lack a systematic approach and limit their scope to only handpicked examples In this work, we analyze the scope and trends of the research on artificial intelligence-based music generation by performing a systematic review of the available publications in the field using the Prisma methodology. Furthermore, we discuss the possible implementations and accessibility of a set of currently available AI solutions, as aids to musical composition. Our research shows how publications are being distributed globally according to many characteristics, which provides a clear picture of the situation of this technology. Through our research it becomes clear that the interest of both musicians and computer scientists in AI-based automatic music generation has increased significantly in the last few years with an increasing participation of mayor companies in the field whose works we analyze. We discuss several generation architectures, both from a technical and a musical point of view and we highlight various areas were further research is needed

Dual Machine-Learning system to aid Glaucoma Diagnosis using disc and cup feature extraction.

Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion of machine learning techniques into the processing of eye fundus images. Several works focus on training a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus images is developed, trained and tested. It consists of two subsystems that are independently trained and tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning and segmentation techniques to detect optic disc and cup independently, combine them and extract their physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are combined to discriminate positive cases of glaucoma and improve final detection. The results show that this system achieves a higher classification rate than previous works. The system also provides information on the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered

An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-

Does Two-Class Training Extract Real Features? A COVID-19 Case Study

Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms.Junta de Andalucía and FEDER research project MSF-PHIA (US-1263715

Sistema informático móvil para el apoyo al cese tabáquico mediante mensajes motivacionales personalizados

El proyecto SmokeFreeBrain estudia la efectividad de una variedad de intervenciones con el objetivo de dejar de fumar usando diferentes técnicas. Uno de los métodos consiste en la aplicación de un protocolo de intervención basado en terapia del comportamiento mediante el uso de aplicaciones móviles, gamificación y envío de mensajes motivacionales de apoyo al paciente en tratamiento para el cese tabáquico o al usuario que está intentando dejar de fumar. Se pretende aprovechar el uso generalizado de los dispositivos móviles en el día a día como una herramienta de control y apoyo, aplicando teoría de juegos al progreso del usuario en su cese tabáquico, y con el uso de un sistema recomendador para seleccionar mensajes motivacionales acordes al perfil del usuario. El sistema desarrollado contempla la creación de una aplicación móvil para llevar a cabo dicho protocolo de intervención, así como el servidor que gestione las peticiones de los usuarios, contenga el sistema recomendador diseñado, tenga la capacidad de mandar mensajes motivacionales a los usuarios y recoja información de uso sobre la aplicación para su futuro análisis.The SmokeFreeBrain studies the effectiveness of a variety of interventions with the goal of quitting smoking by using different techniques. One of the methods consists in the application of an intervention protocol based on behavioral therapy, applying mobile applications, gamification and sending motivational messages of support, both to the patient in treatment for smoking cessation and, to the user who is trying to quit smoking independently. It aims to take advantage of the widespread daily use of mobile devices as a tool for monitor and support, applying game theory to the user progress in their smoking cessation, and recommendation systems for the selection of motivational messages according to the user profile. The system developed for this purpose consists of a mobile application that allows carrying out the intervention protocol as well as the server that manages user requests, implements the recommender system, send motivational messages to users and register usage data to analyze effectiveness.Plan Propio de la Universidad de Sevilla Proyecto: 2017/0000096

Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-

Lower-Limb Falling Detection System Using Gated Recurrent Neural Networks

Accidental falls are one of the most common causes of premature disability and mortality related to unnatural causes. This affects mainly the elderly population. With the current aging of the population, the rate of accidental falls increases. Computer systems for gait analysis and fast assistance in ubiquitous environments can be effective tools to prevent these accidents. In this article we present the advances in the creation of an intelligent device for detecting falls and risk situations based on accelerometer signals registered on the user’s ankle. The proposed method makes use of Deep Learning techniques, specifically Gated Recurrent Neural Networks. The results show that the proposed model is a viable alternative to detect falls and fall risk, which can be implemented in low performance devices for greater autonomy, lower cost and comfortable portability. These results open the possibility of combining fall detection with a biomechanical analysis system to identify gait deficiencies and their relation with falls