Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning

Tesis por compendio[ES] En la última década, el aprendizaje profundo (DL) se ha convertido en la principal herramienta para las tareas de visión por ordenador (CV). Bajo el paradigma de aprendizaje supervisado, y gracias a la recopilación de grandes conjuntos de datos, el DL ha alcanzado resultados impresionantes utilizando redes neuronales convolucionales (CNNs). Sin embargo, el rendimiento de las CNNs disminuye cuando no se dispone de suficientes datos, lo cual dificulta su uso en aplicaciones de CV en las que sólo se dispone de unas pocas muestras de entrenamiento, o cuando el etiquetado de imágenes es una tarea costosa. Estos escenarios motivan la investigación de estrategias de aprendizaje menos supervisadas. En esta tesis, hemos explorado diferentes paradigmas de aprendizaje menos supervisados. Concretamente, proponemos novedosas estrategias de aprendizaje autosupervisado en la clasificación débilmente supervisada de imágenes histológicas gigapixel. Por otro lado, estudiamos el uso del aprendizaje por contraste en escenarios de aprendizaje de pocos disparos para la vigilancia automática de cruces de ferrocarril. Por último, se estudia la localización de lesiones cerebrales en el contexto de la segmentación no supervisada de anomalías. Asimismo, prestamos especial atención a la incorporación de conocimiento previo durante el entrenamiento que pueda mejorar los resultados en escenarios menos supervisados. En particular, introducimos proporciones de clase en el aprendizaje débilmente supervisado en forma de restricciones de desigualdad. Además, se incorpora la homogeneización de la atención para la localización de anomalías mediante términos de regularización de tamaño y entropía. A lo largo de esta tesis se presentan diferentes métodos menos supervisados de DL para CV, con aportaciones sustanciales que promueven el uso de DL en escenarios con datos limitados. Los resultados obtenidos son prometedores y proporcionan a los investigadores nuevas herramientas que podrían evitar la anotación de cantidades masivas de datos de forma totalmente supervisada.[CA] En l'última dècada, l'aprenentatge profund (DL) s'ha convertit en la principal eina per a les tasques de visió per ordinador (CV). Sota el paradigma d'aprenentatge supervisat, i gràcies a la recopilació de grans conjunts de dades, el DL ha aconseguit resultats impressionants utilitzant xarxes neuronals convolucionals (CNNs). No obstant això, el rendiment de les CNNs disminueix quan no es disposa de suficients dades, la qual cosa dificulta el seu ús en aplicacions de CV en les quals només es disposa d'unes poques mostres d'entrenament, o quan l'etiquetatge d'imatges és una tasca costosa. Aquests escenaris motiven la investigació d'estratègies d'aprenentatge menys supervisades. En aquesta tesi, hem explorat diferents paradigmes d'aprenentatge menys supervisats. Concretament, proposem noves estratègies d'aprenentatge autosupervisat en la classificació feblement supervisada d'imatges histològiques gigapixel. D'altra banda, estudiem l'ús de l'aprenentatge per contrast en escenaris d'aprenentatge de pocs trets per a la vigilància automàtica d'encreuaments de ferrocarril. Finalment, s'estudia la localització de lesions cerebrals en el context de la segmentació no supervisada d'anomalies. Així mateix, prestem especial atenció a la incorporació de coneixement previ durant l'entrenament que puga millorar els resultats en escenaris menys supervisats. En particular, introduïm proporcions de classe en l'aprenentatge feblement supervisat en forma de restriccions de desigualtat. A més, s'incorpora l'homogeneïtzació de l'atenció per a la localització d'anomalies mitjançant termes de regularització de grandària i entropia. Al llarg d'aquesta tesi es presenten diferents mètodes menys supervisats de DL per a CV, amb aportacions substancials que promouen l'ús de DL en escenaris amb dades limitades. Els resultats obtinguts són prometedors i proporcionen als investigadors noves eines que podrien evitar l'anotació de quantitats massives de dades de forma totalment supervisada.[EN] In the last decade, deep learning (DL) has become the main tool for computer vision (CV) tasks. Under the standard supervised learnng paradigm, and thanks to the progressive collection of large datasets, DL has reached impressive results on different CV applications using convolutional neural networks (CNNs). Nevertheless, CNNs performance drops when sufficient data is unavailable, which creates challenging scenarios in CV applications where only few training samples are available, or when labeling images is a costly task, that require expert knowledge. Those scenarios motivate the research of not-so-supervised learning strategies to develop DL solutions on CV. In this thesis, we have explored different less-supervised learning paradigms on different applications. Concretely, we first propose novel self-supervised learning strategies on weakly supervised classification of gigapixel histology images. Then, we study the use of contrastive learning on few-shot learning scenarios for automatic railway crossing surveying. Finally, brain lesion segmentation is studied in the context of unsupervised anomaly segmentation, using only healthy samples during training. Along this thesis, we pay special attention to the incorporation of tasks-specific prior knowledge during model training, which may be easily obtained, but which can substantially improve the results in less-supervised scenarios. In particular, we introduce relative class proportions in weakly supervised learning in the form of inequality constraints. Also, attention homogenization in VAEs for anomaly localization is incorporated using size and entropy regularization terms, to make the CNN to focus on all patterns for normal samples. The different methods are compared, when possible, with their supervised counterparts. In short, different not-so-supervised DL methods for CV are presented along this thesis, with substantial contributions that promote the use of DL in data-limited scenarios. The obtained results are promising, and provide researchers with new tools that could avoid annotating massive amounts of data in a fully supervised manner.The work of Julio Silva Rodríguez to carry out this research and to elaborate this dissertation has been supported by the Spanish Government under the FPI Grant PRE2018-083443.Silva Rodríguez, JJ. (2022). Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/190633Compendi

Proportion constrained weakly supervised histopathology image classification

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification. This often does not match the real-world problems where multi-label settings are frequent and possible constraints must be taken into account. In this work, we propose a novel multi-label MIL formulation based on inequality constraints that is able to incorporate prior knowledge about instance proportions. Our method has a theoretical foundation in optimization with logbarrier extensions, applied to bag-level class proportions. This encourages the model to respect the proportion ordering during training. Extensive experiments on a new public dataset of prostate cancer WSIs analysis, SICAP-MIL, demonstrate that using the prior proportion information we can achieve instance-level results similar to supervised methods on datasets of similar size. In comparison with prior MIL settings, our method allows for ∼ 13% improvements in instance-level accuracy, and ∼ 3% in the multi-label mean area under the ROC curve at the bag-level.Spanish Government PID2019-105142RB-C2European Commission 860627Generalitat Valenciana/European Union through the European Regional Development Fund (ERDF) of the Valencian Community IDIFEDER/2020/030Universitat Politecnica de Valenci

Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation

With the recent raise of foundation models in computer vision and NLP, the pretrain-and-adapt strategy, where a large-scale model is fine-tuned on downstream tasks, is gaining popularity. However, traditional fine-tuning approaches may still require significant resources and yield sub-optimal results when the labeled data of the target task is scarce. This is especially the case in clinical settings. To address this challenge, we formalize few-shot efficient fine-tuning (FSEFT), a novel and realistic setting for medical image segmentation. Furthermore, we introduce a novel parameter-efficient fine-tuning strategy tailored to medical image segmentation, with (a) spatial adapter modules that are more appropriate for dense prediction tasks; and (b) a constrained transductive inference, which leverages task-specific prior knowledge. Our comprehensive experiments on a collection of public CT datasets for organ segmentation reveal the limitations of standard fine-tuning methods in few-shot scenarios, point to the potential of vision adapters and transductive inference, and confirm the suitability of foundation models.Comment: MICCAI - MedAGI Workshop 2023. Code in https://github.com/jusiro/fewshot-finetunin

Desarrollo de un sistema de procesamiento de datos para la caracterización de la señal mioeléctrica (sEMG) recogida en ensayos dinámicos en cirujanos durante intervenciones laparoscópicas

[ES] Se ha desarrollado un sistema de procesamiento de datos de señales electromiográficas de superficie (sEMG) en ensayos de laparoscopia dinámica. Esto es, se han caracterizado señales adquiridas en los músculos bíceps, deltoides anterior, trapecio descendente y braquiorradial durante la realización continua de un ejercicio que simula la actividad laparoscópica. El objetivo del sistema es procesar las señales con tal de obtener la evolución temporal de parámetros que aporten una valoración objetiva de la fatiga y el esfuerzo muscular debido al uso de herramientas poco ergonómicas. Tras realizar una revisión bibliográfica de la caracterización de la fatiga en sEMG, los parámetros obtenidos finalmente para ello son valor eficaz (RMS), valor medio absoluto (MAV), frecuencia media (MPF), frecuencia mediana (MDF), ratio de RMS en frecuencia (RMSfreq.ratio) y normalización de momentos espectrales (FInms5). Se ha representado la evolución temporal de estos parámetros a lo largo del ensayo. Además, se ha obtenido un indicador de la calidad del registro: la relación señal ruido (SNR), y se ha aplicado un filtrado de las componentes de interferencia y ruido en las señales, que presentan alto contenido de estas. También se ha obtenido el número de ejercicios realizados y duración de los mismos con tal de evaluar la habilidad del cirujano. Finalmente, se ha desarrollado un manual de usuario del sistema y se han presupuestado los costes asociados a su desarrollo.Silva Rodríguez, JJ. (2017). Desarrollo de un sistema de procesamiento de datos para la caracterización de la señal mioeléctrica (sEMG) recogida en ensayos dinámicos en cirujanos durante intervenciones laparoscópicas. http://hdl.handle.net/10251/85022.TFG

Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

[EN] The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are gaining popularity because they require fewer annotations. In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations. Our method is able to learn from the global WSI diagnosis and a combination of labeled and unlabeled patches. Furthermore, we propose and evaluate an efficient labeling paradigm that guarantees a strong classification performance when combined with our learning framework. We compare our method to SSL and MIL baselines, the state-of-the-art and completely supervised training. With only a small percentage of patch labels our proposed model achieves a competitive performance on SICAPv2 (Cohen's kappa of 0.801 with 450 patch labels), PANDA (Cohen's kappa of 0.794 with 22,023 patch labels) and Camelyon16 (ROC AUC of 0.913 with 433 patch labels). Our code is publicly available at https://github.com/arneschmidt/ssl_and_mil_cancer_classification.This work was supported in part by the European Union's Horizon 2020 Research and Innovation Program through the Marie Skodowska Curie (Cloud Artificial Intelligence For pathologY (CLARIFY) Project) under Grant 860627, and in part by the Spanish Ministry of Science and Innovation under Project PID2019-105142RB-C22.Schmidt, A.; Silva-Rodríguez, J.; Molina, R.; Naranjo Ornedo, V. (2022). Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning. IEEE Access. 10:9763-9773. https://doi.org/10.1109/ACCESS.2022.3143345976397731

Self-learning for weakly supervised Gleason grading of local patterns

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Furthermore, despite the promising results achieved on global scoring the location of cancerous patterns in the tissue is only qualitatively addressed. These heatmaps of tumor regions, however, are crucial to the reliability of CAD systems as they provide explainability to the system's output and give confidence to pathologists that the model is focusing on medical relevant features. Motivated by this, we propose a novel weakly-supervised deeplearning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin, as well as state-of-the-art methods on global biopsylevel scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (kappa) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task. This suggests that the absence of the annotator's bias in our approach and the capability of using large weakly labeled datasets during training leads to higher performing and more robust models. Furthermore, raw features obtained from the patchlevel classifier showed to generalize better than previous approaches in the literature to the subjective global biopsylevel scoring.This work was supported by the Spanish Ministry of Economy and Competitiveness through Projects DPI2016-77869 and PID2019-105142RB-C21.Silva-Rodríguez, J.; Colomer, A.; Dolz, J.; Naranjo Ornedo, V. (2021). Self-learning for weakly supervised Gleason grading of local patterns. IEEE Journal of Biomedical and Health Informatics. 25(8):3094-3104. https://doi.org/10.1109/JBHI.2021.3061457S3094310425

Aplicación de un modelo de gestión de inventarios para incrementar la productividad de la empresa Bmotors SAC 2021

El presente trabajo de investigación tuvo como objetivo general, incrementar la productividad de la empresa Bmotors S.A.C. mediante la aplicación de un modelo de gestión de inventarios. La propuesta surge debido a la falta de un sistema para controlar el stock de la empresa. El estudio inició con la entrevista a la supervisora de repuestos con la finalidad de obtener mayor información respecto a los ítems que maneja la empresa. La encargada en mención brindó la data histórica del año anterior, con dicha data se llevó a cabo la clasificación ABC, donde la clase A fue nuestra muestra de la cual se pudo determinar la participación de cada ítem en los ingresos de la empresa y de igual manera los costos de inventario que estos están generando. Se analizó la demanda y el coeficiente de variación, donde el resultado indicó que el modelo de revisión periódica o modelo “P” es la mejor alternativa para nuestro estudio, puesto que la demanda de repuestos del año 2021 presentó un C.V. mayor al 20%. Para proyectar la demanda del año 2022 a través de pronósticos, se hizo uso de los métodos de regresión lineal, exponencial, polinómica, potencial, logarítmica y de suavización exponencial corregido por tendencia. Los resultados se evaluaron según el menor valor de la desviación Media Absoluta (DAM). Consecuentemente, se calculó el costo total de gestión de inventario para el periodo 2022. Finalmente, se evaluó el impacto económico del modelo de gestión de inventarios propuesto, dando como resultado un incremento en la productividad del 45.28% respecto al año anterior.The general objective of this research work was to increase the productivity of the company Bmotors S.A.C. through the application of an inventory management model. The proposal arises due to the lack of a system to control the stock of the company. The study began with the interview with the spare parts supervisor in order to obtain more information regarding the spare parts handled by the company. The person in charge in mention, provided the historical data of the previous year, with this data the ABC classification was carried out, where class A was our sample from which it was possible to determine the participation of each spare part in the income of the company and in the same way the inventory costs that they are generating. The demand and the coefficient of variation were analyzed, where the result indicated that the periodic review model "P" is the best alternative for our study, since the demand for spare parts in 2021 presented a CV greater than 20%. To project the demand of the year 2022 through forecasts, the methods of linear, exponential, polynomial, potential and simple exponential smoothing regression were used. Outcomes were assessed according to the lowest value of the Mean Absolute Deviation (MAD). Consequently, the total cost of inventory management for the 2022 period was calculated. Finally, the economic impact of the proposed inventory management model was evaluated, resulting in an increase in productivity of 45.28% compared to the previous year.Tesi

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

[EN] Background and Objective: Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Further-more, recent reports indicate that the presence of patterns of the Gleason scale such as the cribriform pattern may also correlate with a worse prognosis compared to other patterns belonging to the Glea-son grade 4. Current clinical guidelines have indicated the convenience of highlight its presence during the analysis of biopsies. All these requirements suppose a great workload for the pathologist during the analysis of each sample, which is based on the pathologist's visual analysis of the morphology and or-ganisation of the glands in the tissue, a time-consuming and subjective task. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies. This analysis must include the Gleason grading of local structures, the detection of cribriform patterns, and the Gleason scoring of the whole biopsy. Methods: The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns based on the Gleason grading system. In particular, we train from scratch a simple self-design architecture with three filters and a top model with global-max pooling. The cribriform pattern is detected by retraining the set of filters of the last convolutional layer in the network. Subsequently, a biopsy-level prediction map is reconstructed by bi-linear interpolation of the patch-level prediction of the Gleason grades. In addition, from the re-constructed prediction map, we compute the percentage of each Gleason grade in the tissue to feed a multi-layer perceptron which provides a biopsy-level score. Results: In our SICAPv2 database, composed of 182 annotated whole slide images, we obtained a Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason grading with the proposed architec-ture trained from scratch. Our results outperform previous ones reported in the literature. Furthermore, this model reaches the level of fine-tuned state-of-the-art architectures in a patient-based four groups cross validation. In the cribriform pattern detection task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset. Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification. Our proposed model is capable of characterising the different Gleason grades in prostate tissue by extracting low-level features through three basic blocks (i.e. convolutional layer + max pooling). The use of global-max pooling to reduce each activation map has shown to be a key factor for reducing complexity in the model and avoiding overfitting. Regarding the Gleason scoring of biopsies, a multi-layer perceptron has shown to better model the decision-making of pathologists than previous simpler models used in the literature.This work was supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA Corporation.Silva-Rodríguez, J.; Colomer, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2020). Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer Methods and Programs in Biomedicine. 195:1-18. https://doi.org/10.1016/j.cmpb.2020.105637S118195Gordetsky, J., & Epstein, J. (2016). Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagnostic Pathology, 11(1). doi:10.1186/s13000-016-0478-2Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. American Journal of Surgical Pathology, 40(2), 244-252. doi:10.1097/pas.0000000000000530Sharma, M., & Miyamoto, H. (2018). Percent Gleason pattern 4 in stratifying the prognosis of patients with intermediate-risk prostate cancer. Translational Andrology and Urology, 7(S4), S484-S489. doi:10.21037/tau.2018.03.20Kweldam, C. F., van der Kwast, T., & van Leenders, G. J. (2018). On cribriform prostate cancer. Translational Andrology and Urology, 7(1), 145-154. doi:10.21037/tau.2017.12.33Remotti, H. (2012). Tissue Microarrays: Construction and Use. Pancreatic Cancer, 13-28. doi:10.1007/978-1-62703-287-2_2KHOUJA, M. H., BAEKELANDT, M., SARAB, A., NESLAND, J. M., & HOLM, R. (2010). Limitations of tissue microarrays compared with whole tissue sections in survival analysis. Oncology Letters, 1(5), 827-831. doi:10.3892/ol_00000145Gertych, A., Ing, N., Ma, Z., Fuchs, T. J., Salman, S., Mohanty, S., … Knudsen, B. S. (2015). Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Computerized Medical Imaging and Graphics, 46, 197-208. doi:10.1016/j.compmedimag.2015.08.002Ren, J., Sadimin, E., Foran, D. J., & Qi, X. (2017). Computer aided analysis of prostate histopathology images to support a refined Gleason grading system. Medical Imaging 2017: Image Processing. doi:10.1117/12.2253887Esteban, Á. E., López-Pérez, M., Colomer, A., Sales, M. A., Molina, R., & Naranjo, V. (2019). A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Computer Methods and Programs in Biomedicine, 178, 303-317. doi:10.1016/j.cmpb.2019.07.003Lucas, M., Jansen, I., Savci-Heijink, C. D., Meijer, S. L., de Boer, O. J., van Leeuwen, T. G., … Marquering, H. A. (2019). Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Archiv, 475(1), 77-83. doi:10.1007/s00428-019-02577-xArvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., … Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific Reports, 8(1). doi:10.1038/s41598-018-30535-1G. Nir, S. Hor, D. Karimi, L. Fazli, B.F. Skinnider, P. Tavassoli, D. Turbin, C.F. Villamil, G. Wang, R.S. Wilson, K.A. Iczkowski, M.S. Lucia, P.C. Black, P. Abolmaesumi, S.L. Goldenberg, S.E. Salcudean, Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts, 2018. 10.1016/j.media.2018.09.005Nir, G., Karimi, D., Goldenberg, S. L., Fazli, L., Skinnider, B. F., Tavassoli, P., … Salcudean, S. E. (2019). Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. JAMA Network Open, 2(3), e190442. doi:10.1001/jamanetworkopen.2019.0442García, G., Colomer, A., & Naranjo, V. (2019). First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. Entropy, 21(4), 356. doi:10.3390/e21040356Ma, Y., Jiang, Z., Zhang, H., Xie, F., Zheng, Y., Shi, H., … Shi, J. (2018). Generating region proposals for histopathological whole slide image retrieval. Computer Methods and Programs in Biomedicine, 159, 1-10. doi:10.1016/j.cmpb.2018.02.020Li, W., Li, J., Sarma, K. V., Ho, K. C., Shen, S., Knudsen, B. S., … Arnold, C. W. (2019). Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging, 38(4), 945-954. doi:10.1109/tmi.2018.2875868Openseadragon, (http://openseadragon.github.io/), Accessed: 10-07-2018.Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213-220. doi:10.1037/h0026256Swets, J. A. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285-1293. doi:10.1126/science.3287615Kweldam, C. F., Nieboer, D., Algaba, F., Amin, M. B., Berney, D. M., Billis, A., … van Leenders, G. J. L. H. (2016). Gleason grade 4 prostate adenocarcinoma patterns: an interobserver agreement study among genitourinary pathologists. Histopathology, 69(3), 441-449. doi:10.1111/his.1297

IMPLICACIONES DE LA CULTURA ORGANIZACIONAL DE INSTITUCIONES DE EDUCACIÓN SUPERIOR EN LA IMPLANTACIÓN DE SISTEMAS DE GESTIÓN DE LA CALIDAD CON RESPONSABILIDAD SOCIAL. IMPLICATIONS OF ORGANIZATIONAL CULTURE OF HIGHER EDUCATION SYSTEMS IN THE IMPLEMENTATION

Los rasgos de la cultura organizacional de las instituciones de educación superior juegan un papel preponderante para facilitar la implementación de propuestas de mejora. El presente artículo muestra los resultados de un estudio de cultura organizacional en una universidad mexicana que se prepara para la implementación de un sistema de gestión de la calidad con responsabilidad social, con la finalidad de reconocer, a través de la adecuación de un modelo de diagnóstico y la aplicación de herramientas de recopilación de datos, cuáles son los elementos culturales que favorecen o limitan dicha implementación. A partir de los resultados se propone un conjunto de estrategias para gestionar los elementos culturales y alinearlos en función de los objetivos estratégicos de la organización.Palabras clave: cultura organizacional, educación superior, calidad, responsabilidad socialAbstractThe features of the organizational culture of higher education institutions play an important role to facilitate the implementation of proposals for improvement. This article shows the results of a study of organizational culture on a MexicanUniversity preparing for the implementation of a quality management social responsibility, in order to recognize, through the adaptation of a model of diagnosis and implementation of data collection tools, what are the cultural elements that favor or limit such implementation. From the results, we propose a set of strategies to manage cultural elements and align them according to the organization's strategic objectives.Keywords: Organizational Culture, Higher Education, Quality, Social Responsibilit