Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures

Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difficult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.Comment: This paper has been accepted at the 22nd Mexican International Conference on Artificial Intelligence (MICAI 2023

Un analisis sobre la efectividad del control interno en el sector público

En el presente artículo se tratará el Control Interno en el sector público, su normatividad, sus principios, el diseño y la implementación de cada una de sus fases. Cómo se ejerce el mismo en las entidades públicas; además, de qué tan efectivo ha sido y si ha logrado disminuir, controlar los fraudes y los delitos de cuello blancos que comúnmente se presentan. Además de esto, si los encargados de aplicarlo son responsables y asumen el compromiso. En Colombia se ha venido experimentando una serie de sucesos lamentables para la economía del país y la efectividad del mismo. A partir de la implementación del MECI, (Modelo Estándar de Control Interno) se ha tratado de establecer un esquema lineal para todas las Empresas Públicas y aquellos particulares que manejen dineros del Estado, trabajen de manera única, logrando uniformidad en los principios y procedimientos de las mismas

Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

There are different channels to communicate the results of a scientific research; however, several research communities state that the Open Access (OA) is the future of acad emic publishing. These Open Ac cess Platforms have adopted OAI - PMH (Open Archives Initiative - the Protocol for Metadata Harvesting) as a standard for communication and interoperability. Nevertheless, it is significant to highlight that the open source know ledge discovery services based on an index of OA have not been developed. Therefore, it is necessary to address Knowledge Discovery (KD) within these platforms aiming at studen ts, teachers and/ or researchers , to recover both , the resources requested and th e resources that are not explicitly requested – which are also appropriate . This objective represents an important issue fo r structured resources under OAI - PMH. This fact is caused because interoperability with other developments carried out outside their implementation environment is generally not a priority (Level 1 "Shared term definitions"). It is here , where the Semantic Web (SW) beco mes a cornerstone of this work. Consequently, we propose OntoOAIV, a semantic approach for the selective knowledge disco very an d visu alization into structured information with OAI - PMH, focused on supporting the activities of scientific or academic research for a specific user. Because of the academic nature of the structured resources with OAI - PMH, the field of application chosen is the context information of a student. Finally, in order to validate the proposed approach, we use the RUDAR (Roskilde University Digital Archive) and REDALYC (Red de Revistas Científicas de América Latina y el Caribe, España y Portugal) repositor ies, which imple ment the OAI - PMH protocol , as well as one s tudent profile for carrying out KD

Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based methods outperform non-DL methods in terms of accuracy but lack explainability. Despite this trade-off, when it comes to making high-stakes decisions, it's important to prioritize understandable Computer-Aided Diagnosis (CADx) that suggests a course of action based on reasonable evidence, rather than a model prescribing a course of action. In this proposal, we learn Prototypical Parts (PPs) per kidney stone subtype, which are used by the DL model to generate an output classification. Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable. In addition, we modify global visual characteristics to describe their relevance to the PPs and the sensitivity of our model's performance. With this, we provide explanations with additional information at the sample, class and model levels in contrast to previous works. Although our implementation's average accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by 1.5 %, our models perform 2.8% better on perturbed images with a lower standard deviation, without adversarial training. Thus, Learning PPs has the potential to create more robust DL models.Comment: This paper has been accepted at the LatinX in Computer Vision Research Workshop at CVPR2023 as a full paper and it will appear on the CVPR proceeding

Desarrollo e implementación de un conjunto didáctico de circuitos electrónicos básicos con fines educativos

Numerosos estudios aseguran que las clases interactivas mejoran el rendimiento de los estudiantes, así como fomentan su interés en el aprendizaje.Los alumnos aprenden más y mejor cuando participan activamente en el proceso de enseñanza/aprendizaje: cuanto mayor es su participación en su propio aprendizaje, más profunda es la comprensión y la retención a largo plazo, ya que lo estudiado pasa a formar parte del andamiaje mismo de su conocimiento. Por lo tanto, con esta iniciativa, se pretende dar protagonismo al alumnado, de forma que pueda probar y comprobar directamente, de su propia mano, lo explicado en clase. Para ello, se implementa físicamente un conjunto de circuitos electrónicos básicos, tanto analógicos como digitales, de forma que los estudiantes puedan verificar la funcionalidad que cada circuito realiza y probar los diversos montajes, analizados y explicados en el aula, a través de un panel de prueba que pueden manejar ellos mismos. Este panel de prueba consiste, para cada diseño desarrollado, en una placa de circuito impreso interactiva en la que los alumnos pueden comprobar el funcionamiento de cada diseño. En esta placa se disponen los diversos componentes que conforman el montaje así como las conexiones para poder comprobar su funcionamiento y medir tanto tensiones como intensidades. De esta forma se dispone de una herramienta didáctica de gran valor educativo que abarca los contenidos más elementales que debe tener cualquier materia sobre electrónica básica; cubriéndose, de esta forma, los conocimientos de asignaturas como “Fundamentos de Electrónica”, presente en las diversas titulaciones de la Escuela Politécnica Superior de la Universidad de Málaga.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies

This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used during the training of the classifier to improve the discrimination power of the features by adding attention layers at the end of each convolutional block. This approach is specifically designed to mimic the morpho-constitutional analysis performed in ex-vivo by biologists to visually identify kidney stones by inspecting both views. The addition of attention mechanisms to the backbone improved the results of single view extraction backbones by 4% on average. Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Estado del arte del proyecto

The aim of REMIND is to create an International and Intersectoral network to facilitate the exchange of staff to progress developments in reminding technologies for persons with dementia that can be deployed in smart environments. The consortium is comprised of an International network of 7 academic beneficiaries, 5 nonacademic beneficiaries and 4 partners from Third Countries, all of whom are committed to progressing the notion of reminding technologies within smart environments. The focus of REMIND is to develop staff and beneficiary/partner skills in the areas of user centered design and behavioral science coupled with improved computational techniques which in turn will offer more appropriate and efficacious reminding solutions. This will be further supported through research involving user centric studies into the use of reminding technologies and the theory of behaviour change to improve compliance of usage. Research objectives will be focused within the domain of smart environments. A smart environment can be viewed as having the ability to sense its surroundings through embedded sensors and following processing of the sensed information, adjust the environment through actuators to offer an improved experience for the inhabitant. Even though the availability, cost, size and battery life of sensing technology have all improved in recent years, the uptake of real smart environments has been limited. This is mainly related to the effort required to support the technical deployments and the lack of a business model to support a service provider capable of offering support to a large number of environments. In addition, there is a limit to the amount of scenarios which can be facilitated by such environments; this limit is directly related to the number of sensors availabl

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on theWeb such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.Project (the SmartSDK project is co-funded by the EU’s Horizon2020 programme under agreement number 723174 - c 2016 EC and the CONACYT’s agreement number 737373) Doctorado IndustrialAdministración y Dirección de EmpresasIngeniería, Industria y ConstrucciónTurism