Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.Spanish Government PID2021-128317OB-I00Government of Andalusia P20-0016

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI-2018-101674-B-I00 and the projects from Junta de Andalucia B-TIC-414, A-TIC-530-UGR20 and P20-00163.In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.Spanish Government RTI-2018-101674-B-I00Junta de Andalucia B-TIC-414 A-TIC-530-UGR20 P20-0016

Advances and challenges in Bioinformatics and Biomedical Engineering: IWBBIO 2020

This Supplement issue, presents five research articles which are distributed, mainly due to the subject they address, from the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), which was held on line, during September, 30th–2nd October, 2020. These contributions have been chosen because of their quality and the importance of their findings. Those contributions were then invited to participate in this supplement for the following journals of BMC: BMC Bioinformatics and BMC Genomics. In the present Editorial in BMC journal, we summarize the contributions that provide a clear overview of the thematic areas covered by the IWBBIO conference, ranging from theoretical/review aspects to real-world applications of bioinformatic and biomedical engineeringPID2021-128317OB-I00 (Ministry of Spain)P20-00163 (FEDER, Junta Andalucia

Nuevos enfoques metodológicos en la enseñanza de las fracciones en la Educación Secundaria para personas adultas: Flipped Classroom

Este Trabajo Fin de Máster intenta reflejar la necesidad de cambio en los procesos metodológicos llevados a cabo en la Educación Secundaria Obligatoria y, concretamente, en la Educación de personas adultas. En él se plantea una propuesta educativa innovadora basada en la metodología Flipped Classroom en la asignatura de Matemáticas del Módulo II del ámbito Científico- Tecnológico y se reflexiona sobre las posibilidades de la implantación real de esta. En el diseño de la propuesta educativa se establece una colaboración con otra estudiante del máster cuyo trabajo aborda el Método Singapur para la enseñanza de las fracciones, reflexionando también de forma conjunta sobre la importancia de la colaboración entre docentes al implementar diferentes metodologías y sobre lo fructífero de su resultado. En este trabajo también se realiza una breve incursión en la investigación educativa en matemáticas.This dissertation that finalises the Master study I have undertaken tries to focus on the importance of changes in the Compulsory Secondary Education, and most importantly, Adults’ Education. The paper proposes an innovative form of education based on the Flipped Classroom Methodology for the Maths Module II of the scientific technology and reflects on the possibilities of the actual implementation of this. The design of the educational proposal is being undertaken in partnership with another master student whose work focuses on the “Singapore Method” to teach fractions as highlighting the importance of the collaboration between lecturers to implement different methodologies and the fruit of their combined results.Departamento de Didáctica de las Ciencias Sociales y ExperimentalesDepartamento de Didáctica de las Ciencias Sociales y ExperimentalesMáster en Profesor de Educación Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanzas de Idioma

Main findings and advances in bioinformatics and biomedical engineeringIWBBIO 2018

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00.In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR)

Enfermería desde la perspectiva del Trabajo Decente

Introduction: The International Labor Organization encourages improvement in the quality of life for workers, highlighting poor working conditions and promoting Decent Work for all.Objective: To reflect on the implications of the work of nursing in relation to the concept of Decent Work.Development: Decent Work responds to the increasing vulnerability of workers, mainly young people, women and the poor. Nursing as a profession is made up mostly of women, faces multiple occupational hazards, including psychosocial risk factors, biological, physical, chemical and ergonomic, which means that this health care profession has higher health risks, is not the best paid job, nor is well recognized socially around the world.Conclusion: Decent Work involves competitive salaries, social security, fair globalization, education, gender equality, low risk, with labor rights and fairness. Nursing professionals, health institutions and governments should reconsider attending to the multiple risks that are faced in any hospital to make nursing work comply with all the features of Decent Work, promoting the reduction of occupational hazards and providing better salaries and working conditions in general to all nurses and improving the quality of their working life.Introducción: La Organización Internacional del Trabajo es un organismo que realiza propuestas y recomendaciones enfocadas a mejorar la calidad de vida de las y los trabajadores, evidenciando las condiciones deficientes de trabajo y enfatiza sobre la importancia de que todo ser humano tenga acceso a un Trabajo Decente, el cual es un concepto con profundos marcos éticos que busca la justicia social.Objetivo: Reflexionar sobre las implicancias del trabajo de Enfermería en relación al Concepto de Trabajo Decente.Desarrollo: El término Trabajo Decente surge como respuesta a la situación de creciente desprotección de los trabajadores(as), principalmente de los jóvenes, las mujeres y los más pobres. La Enfermería como profesión conformada en su mayoría por mujeres, se enfrenta a múltiples riesgos laborales, que la convierten en la profesión sanitaria con mayores riesgos a la salud, pero no en la mejor remunerada, ni en la más reconocida socialmente alrededor del mundo.Consideraciones finales: El Trabajo Decente implica salarios competitivos, seguridad social, globalización equitativa, formación, equidad de género, libre de riesgos, con derechos laborales e igualdad. Los profesionales de Enfermería, las Instituciones de Salud y los Gobiernos, deben reconsiderar las condiciones de trabajo y los múltiples riesgos adicionados a la labor de las Enfermeras, para hacer que la Enfermería cumpla con todas las características de un trabajo decente, fomentando la prevención de riesgos laborales e incentivando con mejores salarios y condiciones de trabajo en general, para mejorar la calidad de vida laboral de las Enfermeras y Enfermeros

New advances in artificial neural networks and machine learning techniques

Peer ReviewedPostprint (published version

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches

This work has been partially supported by the Project PID2021-128317OB-I0, funded by the MCIN/AEI/ 10.13039/501100011033 and ‘‘ERDF A way of making Europe". Funding for open access charge: Universidad de Granada / CBUA. All authors approved the final version of manuscript to be published.Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%.MCIN/AEI/ 10.13039/501100011033/ PID2021-128317OB-I0ERDF A way of making EuropeUniversidad de Granada / CBU

El paradigma emancipatorio y su influencia sobre el desarrollo del conocimiento en Enfermería.

This article arises as a need to reflect on the influence of emancipatory paradigm in the Discipline of Nursing. A literature review was conducted in the databases Scielo, Latindex, Redalyc and written documents (books). It was identified that the emancipatory paradigm as a means of reflection and critique of power traditionally exercised, proposes measures for participation and social justice, including important ethical traces in the human action. In the specific case of Nursing, this emancipatory process reorients the traditional view on the essence of being and doing, so that openness to reflective thinking to modify the way in which the profession is positioned, delivers nursing care and teach future Nurses.El presente artículo surge por la necesidad de reflexionar sobre la influencia del paradigma emancipatorio en la Disciplina de Enfermería. Se realizó una revisión bibliográfica, en las bases de datos Scielo, Latindex, Redalyc, así como el uso de  documentos escritos (libros). Se identificó que el paradigma emancipador, como medio de reflexión y crítica al poder ejercido tradicionalmente,  propone medidas de participación y justicia social, arraigando importantes vestigios éticos en el accionar humano. En el caso específico de Enfermería, este proceso emancipador reorienta la visión tradicionalmente arrastrada en la esencia del ser y hacer, por lo que la apertura al pensamiento reflexivo permite modificar la manera en la cual la profesión se posiciona, brinda intervención y prepara a las (os) futuras (os) enfermeras (os)