Journal papers

2020 – today

  • E. Junquera Álvarez, N. Rico, I. Díaz, S. González, B. Remeseiro. NutriConv: Multitask Learning Framework for Digital Dietary Tracking trained on EFSA’s Pancake Dataset. Neural Computing and Applications, vol. 37, pp. 23833-23862, 2025. [DL] [PDF]
  • L. Gago, M.A. Fernández González, J. Engelmann, B. Remeseiro, L. Igual. Bridging the Quality Gap: Robust Colon Wall Segmentation in Noisy Transabdominal Ultrasound. Computers in Biology and Medicine, vol. 197, pp. 111077, 2025. [DL] [PDF]
  • C. Aumente-Maestro, Michael Müller, B. Remeseiro, J. Díez, M. Reyes. AUDIT: An open-source Python library for AI model evaluation with use cases in MRI brain tumor segmentation. Computer Methods and Programs in Biomedicine, vol. 271, pp. 108991, 2025. [DL] [PDF]
  • A. Betato, H. Díaz Rodríguez, N. French, T. James, B. Remeseiro. MAPunet: High-Resolution Snow Depth Mapping through U-Net Pixel-wise Regression. Remote Sensing Applications: Society and Environment, 37, pp. 101477, 2025. [DL] [PDF]
  • C. Aumente-Maestro, D. Rodríguez González, D. Martínez-Rego, B. Remeseiro. BTS U-Net: A data-driven approach to brain tumor segmentation through deep learning. Biomedical Signal Processing and Control, vol. 104, pp. 107490, 2025. [DL] [PDF]
  • C. Aumente-Maestro, J. Díez, B. Remeseiro. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. Computer Methods and Programs in Biomedicine, vol. 260, pp. 108540, 2025. [DL] [PDF]
  • M.M. Vila, L. Gago, P. Pérez-Sánchez, M. Grau, B. Remeseiro, L. Igual. Deep-stratification of the cardiovascular risk by ultrasound carotid artery images. Biomedical Signal Processing and Control, vol. 91, pp. 106035, 2024. [DL] [PDF]
  • G. de la Cruz, M. Lira, O. Luaces, B. Remeseiro. Eye-LRCN: a Long-term Recurrent Convolutional Network for Eye Blink Completeness Detection. IEEE Transactions on Neural Networks and Learning Systems, vol. 35 (4), pp. 5130-5140, 2024. [DL] [PDF]
  • P. Pérez-Núñez, J. Diez, B. Remeseiro, O. Luaces, A. Bahamonde. All-in-one picture: visual summary of items in a recommender system. Neural Computing and Applications, vol. 35, pp. 20339–20349, 2023. [DL] [PDF]
  • P. Pérez-Núñez, J. Diez, O. Luaces, B. Remeseiro, A. Bahamonde. Users’ photos of items can reveal their tastes in a recommender system. Information Sciences, vol. 642, pp. 119227, 2023. [DL] [PDF]
  • E. Blanco-Mallo, L. Moran-Fernandez, B. Remeseiro, V. Bolon-Canedo. Do all roads lead to Rome? Studying distance measures in the context of machine learning. Pattern Recognition, vol. 141, pp. 109646, 2023. [DL] [PDF]
  • M.M. Vila, L. Igual, B. Remeseiro, R. Elosua, R. Ramos, J.M. Valdivieso, R. Martí, J. Marrugat, M. Grau. Polyvascular subclinical atherosclerosis: correlation between ankle brachial index and carotid atherosclerosis in a population-based sample. Angiology, vol. 74 (5), pp. 443-451, 2023. [DL]
  • E. Aguilar, B. Nagarajan, B. Remeseiro, P. Radeva. Bayesian Deep Learning for Semantic Segmentation of Food Images. Computers & Electrical Engineering, vol. 103, pp. 108380, 2022. [DL] [PDF]
  • L. Gago, M.M. Vila, M. Grau, B. Remeseiro, L. Igual. An end-to-end framework for intima media measurement and atherosclerotic plaque detection in the carotid artery. Computer Methods and Programs in Biomedicine, vol. 223, pp. 106954, 2022. [DL] [PDF]
  • D. Morales, E.Talavera, B. Remeseiro. Playing to distraction: towards a robust training of CNN classifiers. Neural Computing and Applications, vol. 33, pp. 16937-16949, 2021. [DL] [PDF]
  • M. M. Vila, B. Remeseiro, L. Igual, R. Elosua, R. Ramos, J. M. Valdivielso, R. Martí-Lluch, J. Marrugat, M. Grau. Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness? Hypertension Research, vol. 44, pp. 978-987, 2021. [DL] [PDF]
  • B. Remeseiro, A.M. Mendonça, A. Campilho. Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation. The Visual Computer, vol. 37, pp. 1247-1261, 2021. [DL] [PDF]
  • M. Torre, B. Remeseiro, P. Radeva, F. Martínez. DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 726-737, 2020. [DL]
  • V. Bolón-Canedo, B. Remeseiro. Feature selection in image analysis: a survey. Artificial Intelligence Review, 2020, vol. 53, pp. 2905-2931. [DL] [PDF]
  • M. M. Vila, B. Remeseiro, M. Grau, R. Elosua, A. Betriu, E. Fernandez-Giraldez, L. Igual. Semantic Segmentation with DenseNets for Carotid Artery Ultrasound Plaque Segmentation and CIMT estimation. Artificial Intelligence in Medicine, vol. 103, pp. 101784, 2020. [DL]
  • J. Díez, P. Pérez-Núñez, O. Luaces, B. Remeseiro, A. Bahamonde. Towards Explainable Personalized Recommendations by Learning from Users’ Photos. Information Sciences, vol. 520, pp. 416-430, 2020. [DL]

2010 – 2019

  • B. Remeseiro, V. Bolón-Canedo. A review of feature selection methods in medical applications. Computers in Biology and Medicine, vol. 112, pp. 103375, 2019. [DL]
  • B. Remeseiro, J. Tarrío-Saavedra, M. Francisco-Fernández, M. G. Penedo, S. Naya, R. Cao. Automatic detection of defective crankshafts by image analysis and supervised classification. The International Journal of Advanced Manufacturing Technology, vol. 105 (9), pp. 3761-3777, 2019. [DL] [PDF]
  • E. Aguilar, B. Remeseiro, M. Bolaños, P. Radeva. Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants. IEEE Transactions on Multimedia, vol. 20 (12), pp. 3266 – 3275, 2018. [DL]
  • D. Peteiro-Barral, B. Remeseiro, R. Méndez, M.G. Penedo. Evaluation of an automatic dry eye test using MCDM methods and rank correlation. Medical & Biological Engineering & Computing, vol. 55 (4), pp. 527-536, 2017. [DL]
  • J. González-Domínguez, B. Remeseiro, M.J. Martín. Parallel Definition of Tear Film Maps on Distributed-Memory Clusters for the Support of Dry Eye Diagnosis. Computer Methods and Programs in Biomedicine, vol. 139, pp. 51-60, 2017. [DL]
  • B. Remeseiro, A. Mosquera, M.G. Penedo. CASDES: A Computer-Aided System to Support Dry Eye Diagnosis Based on Tear Film Maps. IEEE Journal of Biomedical and Health Informatics, vol. 20 (3), pp. 936-943, 2016. [DL]
  • B. Remeseiro, N. Barreira, C. García-Resúa, M. Lira, M.J. Giráldez, E. Yebra-Pimentel, M.G. Penedo. iDEAS: a web-based system for dry eye assessment. Computer Methods and Programs in Biomedicine, vol. 130, pp. 186-197, 2016. [DL]
  • B. Remeseiro, K.M. Oliver, A. Tomlinson, E. Martin, N. Barreira, A. Mosquera. Automatic grading system for human tear films. Pattern Analysis and Applications, vol. 18 (3), pp. 677-694, 2015. [DL]
  • B. Remeseiro, V. Bolón-Canedo, D. Peteiro-Barral, A. Alonso-Betanzos, B. Guijarro-Berdiñas, A. Mosquera, M.G. Penedo, N. Sánchez-Maroño. A Methodology for Improving Tear Film Lipid Layer Classification. IEEE Journal of Biomedical and Health Informatics, vol. 18 (4), pp. 1485-1493, 2014. [DL]
  • C. García-Resúa, H. Pena-Verdeal, B. Remeseiro, M.J. Giráldez, E. Yebra-Pimentel. Correlation between tear osmolarity and tear meniscus. Optometry and Vision Science, vol. 91 (12), pp. 1419-1429, 2014. [DL]
  • B. Remeseiro, M. Penas, N. Barreira, A. Mosquera, J. Novo, C. García-Resúa. Automatic classification of the interferential tear film lipid layer using colour texture analysis. Computer Methods and Programs in Biomedicine, vol. 111, pp. 93-103, 2013. [DL]
  • B. Remeseiro, M. Penas, A. Mosquera, J. Novo, M.G. Penedo, E. Yebra-Pimentel. Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification. Computational and Mathematical Methods in Medicine, vol. 2012, art. 207315, pp. 1-10, 2012. [DL]
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