@conference {cAnguera25, title = {Quantum Machine Learning for Intrusion Detection in Software-Defined Networks}, booktitle = {1st Intl. Workshop on Quantum Computing and Artificial Intelligence (QC+AI 2025) }, year = {2025}, month = {03/2025}, publisher = {Association for the Advancement of Artificial Intelligence (AAAI)}, organization = {Association for the Advancement of Artificial Intelligence (AAAI)}, address = {Philadelphia}, abstract = {

Quantum machine learning (QML) algorithms for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their
classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts.\  This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.

}, url = {https://sites.google.com/view/qcai2025}, author = {Joan Lo Anguera and Jos{\'e} Antonio L{\'a}zaro and Ruiz-Hidalgo, J. and Sol{\'e}, A. and Casas, J. and Samuel Sarmiento and Adolfo Ler{\'\i}n} } @article {aa, title = {QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation--Analysis of Ranking Metrics and Benchmarking Results}, journal = {Journal of Machine Learning for Biomedical Imaging}, year = {2022}, month = {08/2022}, abstract = {

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at:\ this https URL.

}, url = {https://www.melba-journal.org/papers/2022:026.html}, author = {Raghav Metha and Angelos Filos and Ujjwal Baid and Laura Mora and Ver{\'o}nica Vilaplana and Christos Davatzikos and Bjoern Menze and Spyridon Bakas and Yarin Gal and Tar Arbel} } @conference {cCabezas, title = {Quality Control in Crowdsourced Object Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, abstract = {

This paper explores processing techniques to deal with noisy data in crowdsourced object segmentation tasks. We use the data collected with "Click{\textquoteright}n{\textquoteright}Cut", an online interactive segmentation tool, and we perform several experiments towards improving the segmentation results. First, we introduce different superpixel-based techniques to filter users{\textquoteright} traces, and assess their impact on the segmentation result. Second, we present different criteria to detect and discard the traces from potential bad users, resulting in a remarkable increase in performance. Finally, we show a novel superpixel-based segmentation algorithm which does not require any prior filtering and is based on weighting each user{\textquoteright}s contribution according to his/her level of expertise.

Selected among Top 10\% papers in ICIP 2015 based on the reviewer scores and recommendations.

[Preprint on arXiv]

[Related BSc thesis by Ferran Cabezas]

[IEEE ICIP 2015 conference website]

Co-filtering human interaction and object segmentation from Xavier Giro

}, url = {http://arxiv.org/abs/1505.00145}, author = {Cabezas, Ferran and Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent} } @article {aBonet-Carne14a, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {45}, year = {2015}, pages = {427{\textendash}433}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @article {aBonet-Carne14, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {44}, year = {2014}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @article {aSole07, title = {Quadratic Interpolation and Linear Lifting Design}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {1}, number = {1}, year = {2007}, pages = {1{\textendash}9}, issn = {1110-8657}, author = {Sol{\'e}, J. and Salembier, P.} }