IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction

Intelligent Personal Assistants Solutions in Ubiquitous Environments in the Context of Internet of Things

Internet of Things (IoT) will create the opportunity to develop new types of businesses. Every tangible object, biologic or not, will be identified by a unique address, creating a common network composed by billions of devices. Those devices will have different requirements, creating the necessity of finding new mechanisms to satisfy the needs of all the entities within the network. This is one of the main problems that all the scientific community should address in order to make Internet of Things the Future Internet. Currently, IoT is used in a lot of projects involving Wireless Sensor Networks (WSNs). Sensors are generally cheap and small devices able to generate useful information from physical indicators. They can be used on smart home scenarios, or even on healthcare environments, turning sensors into useful devices to accomplish the goals of many use case scenarios. Sensors and other devices with some reasoning capabilities, like smart objects, can be used to create smart environments. The interaction between the objects in those scenarios and humans can be eased by the inclusion of Intelligent Personal Assistants (IPAs). Currently, IPAs have good reasoning capabilities, improving the assistance they give to their owners. Artificial intelligence (AI), new learning mechanisms, and the evolution assisted in speech technology also contributed to this improvement. The integration of IPAs in IoT scenarios can become a case of great success. IPAs will comprehend the behavior of their owners not only through direct interactions, but also by the interactions they have with other objects in the environment. This may create ubiquitous communication scenarios where humans act as passive elements, being adequately informed of all the aspects of interest that surrounds them. The communication between IPAs and other objects in their surrounding environment may use gateways for traffic forwarding. On ubiquitous environments devices can be mobile or static. For example, in smart home scenarios, objects are generally static, being always on the same position. In mobile health scenarios, objects can move from one place to another. To turn IPAs useful on all types of environments, static and mobile gateways should be developed. On this dissertation, a novel mobile gateway solution for an IPA platform inserted on an IoT context is proposed. A mobile health scenario was chosen. Then, a Body Sensor Network (BSN) is always monitoring a person, giving the real time feedback of his/her health status to another person responsible by him (designated caretaker). On this scenario, a mobile gateway is needed to forward the traffic between the BSN and the IPA of the caretaker. Therefore, the IPA is able to give warnings about the health status of the person under monitoring, in real time. The proposed system is evaluated, demonstrated, and validated through a prototype, where the more important aspects for IPAs and IoT networks are considered

Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions

Federated learning is a groundbreaking distributed machine learning paradigm that allows for the collaborative training of models across various entities without directly sharing sensitive data, ensuring privacy and robustness. This Ph.D. dissertation delves into the intricacies of federated learning, investigating the algorithmic and data-driven challenges of deep learning models in the presence of additive noise in this framework. The main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. A key contribution of this study is introducing specific metrics for training with noise. Signal-to-Noise Ratio (SNR) is introduced as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion mechanisms to enhance privacy without compromising performance. This research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. The present study also explores a real-world application of federated learning in weather prediction applications that suffer from the issue of imbalanced datasets. Utilizing data from multiple sources combined with advanced data augmentation techniques improves the accuracy and generalization of weather prediction models, even when dealing with imbalanced datasets. Overall, federated learning is pivotal in harnessing decentralized datasets for real-world applications while safeguarding privacy. By leveraging noise as a tool for regularization and privacy enhancement, this research study aims to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy

Generalizable deep learning based medical image segmentation

Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

Risk Assessment of Hip Fracture Based on Machine Learning

[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.Galassi, A.; Martín-Guerrero, JD.; Villamor, E.; Monserrat Aranda, C.; Rupérez Moreno, MJ. (2020). Risk Assessment of Hip Fracture Based on Machine Learning. 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Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Pellicer-Valero, O. J., Rupérez, M. J., Martínez-Sanchis, S., & Martín-Guerrero, J. D. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications, 143, 113083. doi:10.1016/j.eswa.2019.113083Martínez-Martínez, F., Rupérez-Moreno, M. J., Martínez-Sober, M., Solves-Llorens, J. A., Lorente, D., Serrano-López, A. J., … Martín-Guerrero, J. D. (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine, 90, 116-124. doi:10.1016/j.compbiomed.2017.09.019Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi:10.7861/futurehosp.6-2-94Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Ho-Le, T. P., Center, J. R., Eisman, J. A., Nguyen, T. V., & Nguyen, H. T. (2017). Prediction of hip fracture in post-menopausal women using artificial neural network approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2017.8037784Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Testi, D., Viceconti, M., Baruffaldi, F., & Cappello, A. (1999). Risk of fracture in elderly patients: a new predictive index based on bone mineral density and finite element analysis. Computer Methods and Programs in Biomedicine, 60(1), 23-33. doi:10.1016/s0169-2607(99)00007-3Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Luo, Y., Ahmed, S., & Leslie, W. D. (2018). Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk. Computer Methods and Programs in Biomedicine, 155, 75-83. doi:10.1016/j.cmpb.2017.11.020Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Ferizi, U., Besser, H., Hysi, P., Jacobs, J., Rajapakse, C. S., Chen, C., … Chang, G. (2018). Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. Journal of Magnetic Resonance Imaging, 49(4), 1029-1038. doi:10.1002/jmri.26280Villamor, E., Monserrat, C., Del Río, L., Romero-Martín, J. A., & Rupérez, M. J. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine, 193, 105484. doi:10.1016/j.cmpb.2020.105484Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. 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(2011). The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study. Osteoporosis International, 23(2), 513-520. doi:10.1007/s00198-011-1569-2BowyerK. W.ChawlaN. V.HallL. O.KegelmeyerW. P.SMOTE: synthetic minority over-sampling techniqueCoRRhttps://arxiv.org/abs/1106.181

A critical analysis of an IoT—aware AAL system for elderly monitoring

Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

Automated Correlative Light and Electron Microscopy using FIB-SEM as a tool to screen for ultrastructural phenotypes

In Correlative Light and Electron Microscopy (CLEM), two imaging modalities are combined to take advantage of the localization capabilities of light microscopy (LM) to guide the capture of high-resolution details in the electron microscope (EM). However, traditional approaches have proven to be very laborious, thus yielding a too low throughput for quantitative or exploratory studies of populations. Recently, in the electron microscopy field, FIB-SEM (Focused Ion Beam -Scanning Electron Microscope) tomography has emerged as a flexible method that enables semi-automated 3D volume acquisitions. During my thesis, I developed CLEMSite, a tool that takes advantage of the semi-automation and scanning capabilities of the FIB-SEM to automatically acquire volumes of adherent cultured cells. CLEMSite is a combination of computer vision and machine learning applications with a library for controlling the microscope ( product from a collaboration with Carl Zeiss GmbH and Fibics Inc.). Thanks to this, the microscope was able to automatically track, find and acquire cell regions previously identified in the light microscope. More specifically, two main modules were implemented. First, a correlation module was designed to detect and record reference points from a grid pattern present on the culture substrate in both modalities (LM and EM). Second, I designed a module that retrieves the regions of interest in the FIB-SEM and that drives the acquisition of image stacks between different targets in an unattended fashion. The automated CLEM approach is demonstrated on a project where 3D EM volumes are examined upon multiple siRNA treatments for knocking down genes involved in the morphogenesis of the Golgi apparatus. Additionally, the power of CLEM approaches using FIB-SEM is demonstrated with the detailed structural analysis of two events: the breakage of the nuclear envelope within constricted cells and an intriguing catastrophic DNA Damage Response in binucleated cells. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible and that EM can provide incredible insights to guide new biological discoveries

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201