Consolidated Research Group
Artificial Intelligence and Bio-Medical Applications
About us
We are the "Artificial Intelligence and Bio-Medical Applications (AIBA)" Consolidated research group at Universitat de Barcelona, Ref. (2021SGR01094) Recognized by Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR), located in Catalonia, Spain.
Latest News
Big Achievements in CVPR2026
This year CVPR 2026 we have 1 CVPR main conference paper, 1 CVPR findings paper, 1 Workshop paper, 1 Challenge winner, 1 MetaFood Workshop paper, 1 MetaFood Workshop extended abstract selected.
Desmitificant la intel·ligència artificial 2026
Organizando una escuela de verano en España sobre "Desmitificant la intel·ligència artificial"
laCaixa InPhINIT Fellowship Candidate
Our Predoc Research Salma has been awarded the prestigious InPhINIT Fellowship from laCaixa Foundation
Team Members
Research Lines
Deep Learning
Deep learning, a cutting-edge branch of artificial intelligence, empowers machines to learn from vast datasets and make intelligent decisions. By mimicking the structure and function of the human brain through artificial neural networks, deep learning algorithms excel at tasks such as image recognition, speech synthesis, and language translation. In our team, we harness the potential of deep learning to drive innovation and solve complex problems across various domains.
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Deep learning for food computing
Today Deep learning (DL) is revolutionizing many fields of real problems, beating human performance in problems such as object recognition, lip reading, or cancer detection. However, food recognition and segmentation are still underexplored and underexploited. To address them, we develop highly robust DL algorithms employing Transfer learning, Transformers, Uncertainty modeling and Explainability among others.
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Pathophysiology of Nervous System Diseases
Our team is dedicated to unraveling the complex mechanisms underlying neurological disorders, from Alzheimer's disease to Parkinson's disease and beyond. Through cutting-edge research and interdisciplinary collaboration, we're gaining invaluable insights into the molecular, cellular, and physiological changes that contribute to these conditions. Together, we're working towards innovative treatments and therapies to improve the lives of those affected by these disorders.
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Medical Imaging
Our team is committed to harnessing the power of advanced imaging technologies to improve healthcare outcomes. From X-rays and MRI scans to CT scans and ultrasound, medical imaging plays a crucial role in diagnosis, treatment planning, and monitoring of various medical conditions. Through cutting-edge research and innovation, we're pushing the boundaries of what's possible in medical imaging, developing new techniques and algorithms to enhance image quality, reduce radiation exposure, and increase diagnostic accuracy.
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Artificial Intelligence applied to Health
Our team is dedicated to harnessing the power of AI to revolutionize healthcare. From predictive analytics and diagnostic imaging to personalized treatment recommendations and remote patient monitoring, AI holds the potential to transform every aspect of the healthcare ecosystem. Through cutting-edge research and collaboration with healthcare professionals, we aim to develop innovative AI solutions that improve patient outcomes, increase efficiency, and reduce costs.
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Computer Graphics
Our team is passionate about creating visually stunning and immersive experiences through the power of digital imagery. From video games and animation to virtual reality and augmented reality, computer graphics play a vital role in shaping how we interact with digital content. With a focus on innovation and creativity, our team is dedicated to pushing the boundaries of what's possible in computer graphics, developing cutting-edge techniques and technologies to bring ideas to life in breathtaking detail.
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Machine Learning
We're passionate about harnessing the power of data and algorithms to create intelligent systems that can learn and adapt. From predictive analytics and recommendation systems to autonomous vehicles and medical diagnostics, machine learning is revolutionizing industries and driving innovation. With a focus on cutting-edge research and practical applications, our team is dedicated to pushing the boundaries of what's possible with machine learning, developing new algorithms and techniques to solve complex problems and unlock new opportunities.
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Data Science & Bio-Medical Applications
We're passionate about leveraging data-driven approaches to tackle some of the most pressing challenges in healthcare and life sciences. From analyzing genomic data and identifying biomarkers to predicting disease outcomes and optimizing treatment strategies, data science holds immense potential for revolutionizing biomedical research and patient care. With a focus on cutting-edge methodologies and interdisciplinary collaboration, our team is dedicated to pushing the boundaries of what's possible at the intersection of data science and biomedicine.
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Natural Language Processing (NLP)
We're passionate about harnessing the power of language to create intelligent systems that can understand, interpret, and generate human language. From chatbots and virtual assistants to language translation and sentiment analysis, NLP is revolutionizing how we communicate and interact with technology. With a focus on cutting-edge research and practical applications, our team is dedicated to pushing the boundaries of what's possible with NLP, developing new algorithms and techniques to solve complex language-related problems and unlock new opportunities.
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Statistical and Probabilistic Modeling
Our team is more passionate about harnessing the power of data and probability theory to gain insights, make predictions, and drive decision-making in various fields. From predicting financial trends and analyzing market behavior to modeling complex systems in engineering and science, statistical and probabilistic modeling plays a crucial role in understanding uncertainty and variability. With a focus on cutting-edge methodologies and practical applications, our team is dedicated to pushing the boundaries of what's possible with statistical and probabilistic modeling, developing innovative solutions to solve complex problems and unlock new opportunities.
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Uncertainty Modeling
We're passionate about understanding and quantifying uncertainty in various systems and processes. From predicting the reliability of engineering structures to assessing risk in financial markets and forecasting the impact of climate change, uncertainty modeling and quantification are essential for making informed decisions in the face of uncertainty. With a focus on cutting-edge methodologies and practical applications, our team is dedicated to pushing the boundaries of what's possible in uncertainty modeling and quantification, developing innovative solutions to address complex challenges and mitigate risks.
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Bio-Mechanics
We're dedicated to understanding the mechanical principles underlying biological systems, from the movement of cells and tissues to the biomechanics of human movement and injury prevention. With a focus on interdisciplinary research and practical applications, our team is committed to pushing the boundaries of what's possible in biomechanics, developing innovative solutions to address challenges in healthcare, sports performance, and rehabilitation.
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Bio-Informatics
We're dedicated to leveraging computational tools and techniques to analyze biological data, unlocking new insights and driving discoveries in the life sciences. From studying DNA sequences and protein structures to understanding complex biological pathways and disease mechanisms, bioinformatics plays a crucial role in advancing our understanding of living organisms and their interactions. With a focus on cutting-edge research and practical applications, our team is committed to pushing the boundaries of what's possible in bioinformatics, developing innovative solutions to address key challenges in healthcare, agriculture, and environmental science.
More about this lineLatest publications
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis
2026 | Imanol G Estepa, Jesús M Rodríguez-de-Vera, Ignacio Sarasúa, Bhalaji Nagarajan, Petia Radeva
Integration of Calcium Imaging Traces via Deep Generative Modeling
2026 | Berta Ros, Mireia Olives-Verger, Caterina Fuses, Josep M Canals, Jordi Soriano, Jordi Abante
snputils: A High-Performance Python Library for Genetic Variation and Population Structure
2026 | David Bonet, Marçal Comajoan Cara, Míriam Barrabés, Riccardo Smeriglio, Devang Agrawal, Khaled Aounallah, Margarita Geleta, Albert Dominguez Mantes, Christophe Thomassin, Cole Shanks, Edward C Huang, Marc Franquesa Monés, Aina Luis, Joan Saurina, Maria Perera, Cayetana López, Benet Oriol Sabat, Jordi Abante, Sonia Moreno-Grau, Daniel Mas Montserrat, Alexander G Ioannidis
ARGformer: learning on ancestral recombination graphs with transformers
2026 | David Bonet, Cole Shanks, Marçal Comajoan Cara, Jordi Abante, Alexander G Ioannidis
Physically Informed 3D Food Reconstruction: Methods and Results
2026 | Jiangpeng He, Yuhao Chen, Gautham Vinod, Xiaoyan Zhang, Talha Ibn Mahmud, Ahmad AlMughrabi, Umair Haroon, Ricardo Marques, Petia Radeva, Yawei Jueluo, Chengyu Shi, Pengyu Wang, Pengcheng Xi, Alexander Wong, Fengqing Zhu
VolE: A point-cloud framework for food 3D reconstruction and volume estimation
2026 | Umair Haroon, Ahmad AlMughrabi, Thanasis Zoumpekas, Ricardo Marques, Petia Radeva
BenchSeg: A Large-Scale Dataset and Benchmark for Multi-View Food Video Segmentation
2026 | Ahmad AlMughrabi, Guillermo Rivo, Carlos Jiménez-Farfán, Umair Haroon, Farid Al-Areqi, Hyunjun Jung, Benjamin Busam, Ricardo Marques, Petia Radeva
How the international treaty on plant genetic resources for food and agriculture can support effective germplasm exchange: four Colombian case studies
2026 | Tatiana Rivera, Robert Andrade, Carolina Gonzalez, Daniel Ortiz, Jhon Ocampo, Diana Córdoba
Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis
2026 | Anna Oliveras, Roger Marí, Rafael Redondo, Oriol Guardià, Cynthia Ifeyinwa Ugwu, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
2026 | Nicholas Konz, Richard Osuala, Preeti Verma, Yuwen Chen, Hanxue Gu, Haoyu Dong, Yaqian Chen, Andrew Marshall, Lidia Garrucho, Kaisar Kushibar, Daniel M Lang, Gene S Kim, Lars J Grimm, John M Lewin, James S Duncan, Julia A Schnabel, Oliver Diaz, Karim Lekadir, Maciej A Mazurowski | Medical Image Analysis, 103943, 2026
Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline
2026 | Ismail Mese, Tugba Akinci D’Antonoli, Christian Bluethgen, Keno Bressem, Renato Cuocolo, Akshay Chaudhari, Ali S Tejani, Amanda Isaac, Andrea Ponsiglione, Aymen Meddeb, Bardia Khosravi, Bastien Le Guellec, Charles E Kahn Jr, Chong Hyun Suh, Daniel Pinto Dos Santos, Dow-Mu Koh, Eleftherios Tzanis, Elmar Kotter, Errol Colak, Felipe Kitamura, Felix Busch, Felix Nensa, Guang Yang, Henning Müller, Jakob Nikolas Kather, Jawed Nawabi, Jens Kleesiek, Jingyu Zhong, João Santinha, Johannes Haubold, José Guilherme de Almeida, Karim Lekadir, Kostas Marias, Lara Noelle Reiner, Lena Maier-Hein, Linda Moy, Lisa C Adams, Luis Martí-Bonmatí, Magdalini Paschali, Mana Moassefi, Matthias Dietzel, Merel Huisman, Michael Ingrisch, Michail E Klontzas, Nikolaos Papanikolaou, Oliver Diaz, Paulo Kuriki, Philipp Seeböck, Pouria Rouzrokh, Quirin D Strotzer, Seong Ho Park, Shahriar Faghani, Soroosh Tayebi Arasteh, Su Hwan Kim, Vasantha Kumar Venugopal, Woojin Kim, Burak Kocak | Diagnostic and Interventional Radiology, 2026
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
2026 | Lidia Garrucho, Smriti Joshi, Kaisar Kushibar, Richard Osuala, Maciej Bobowicz, Xavier Bargalló, Paulius Jaruševičius, Kai Geissler, Raphael Schäfer, Muhammad Alberb, Tony Xu, Anne Martel, Daniel Sleiman, Navchetan Awasthi, Hadeel Awwad, Joan C Vilanova, Robert Martí, Daan Schouten, Jeong Hoon Lee, Mirabela Rusu, Eleonora Poeta, Luisa Vargas, Eliana Pastor, Maria A Zuluaga, Jessica Kächele, Dimitrios Bounias, Alexandra Ertl, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M Abo-Elhoda, Sara W Tantawy, Shorouq S Sakrana, Norhan O Shawky-Abdelfatah, Amr Muhammad Abdo-Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Carlos Martín-Isla, Oliver Díaz, Laura Igual, Karim Lekadir | arXiv preprint arXiv:2603.01250, 2026
From data to treatment plan: An AI-driven path for automated breast radiotherapy planning
2026 | P Gallego, E Ambroa, J Pérez-Alija, JC Julià, N Jornet, A Matas, C Anson, A Mera, N Tejedor, H Vivancos, A Ruiz, M Barceló, A Dominguez, V Riu, J Roda, P Carrasco, S Balocco, O Díaz | Journal of applied clinical medical physics 27 (3), e70491, 2026
Artificial intelligence in cardiovascular imaging: risks, mitigations and the path to safe implementation
2026 | James P Howard, Qiang Zhang, Ahmed M Salih, Steffen E Petersen, Karim Lekadir, Zahra Raisi-Estabragh | Heart 112 (5), 246-252, 2026
IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry
2026 | Jieyun Bai, Yitong Tang, Xiao Liu, Jiale Hu, Yunda Li, Xufan Chen, Yufeng Wang, Chen Ma, Yunshu Li, Bowen Guo, Jing Jiao, Yi Huang, Kun Wang, Lifei Li, Yuzhang Ma, Xiaoxin Han, Haochen Shao, Zi Yang, Qingchen Liu, Yuchen Hu, Jingfan Kuang, Shanglin Song, Anirvan Krishna, Zaid Ahmed Khan, Zelan Li, Zhengyang Zhang, Hansen Zhang, Yan Cheng, Xuezhi Zhang, Xi Chen, Hao Yan, Lyuyang Tong, Bo Du, Bo Deng, Yu Chen, Zilun Peng, Saeid Rezaei, Jie Gan, Weidong Cai, Fangyijie Wang, Kathleen M Curran, Guénolé Silvestre, Isaac Khobo, Yaosheng Lu, Dong Ni, Yuxin Huang, Mohammad Yaqub, Jun Ma, Karim Lekadir, Shuo Li | Medical Image Analysis, 103960, 2026
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
2026 | Jieyun Bai, Yitong Tang, Zihao Zhou, Mahdi Islam, Musarrat Tabassum, Enrique Almar-Munoz, Hongyu Liu, Hui Meng, Nianjiang Lv, Bo Deng, Yu Chen, Zilun Peng, Yusong Xiao, Li Xiao, Nam-Khanh Tran, Dac-Phu Phan-Le, Hai-Dang Nguyen, Xiao Liu, Jiale Hu, Mingxu Huang, Jitao Liang, Chaolu Feng, Xuezhi Zhang, Lyuyang Tong, Bo Du, Ha-Hieu Pham, Thanh-Huy Nguyen, Min Xu, Juntao Jiang, Jiangning Zhang, Yong Liu, Md Kamrul Hasan, Jie Gan, Zhuonan Liang, Weidong Cai, Yuxin Huang, Gongning Luo, Mohammad Yaqub, Karim Lekadir | arXiv preprint arXiv:2601.15572, 2026
Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos
2026 | Jieyun Bai, Zihao Zhou, Yitong Tang, Jie Gan, Zhuonan Liang, Jianan Fan, Lisa B Mcguire, Jillian L Clarke, Weidong Cai, Jacaueline Spurway, Yubo Tan, Shiye Wang, Wenda Shen, Wangwang Yu, Yihao Li, Philippe Zhang, Weili Jiang, Yongjie Li, Salem Muhsin Ali Binqahal Al Nasi, Arsen Abzhanov, Numan Saeed, Mohammad Yaqub, Zunhui Xia, Hongxing Li, Libin Lan, Jayroop Ramesh, Valentin Bacher, Mark Eid, Hoda Kalabizadeh, Christian Rupprecht, Ana IL Namburete, Pak-Hei Yeung, Madeleine K Wyburd, Nicola K Dinsdale, Assanali Serikbey, Jiankai Li, Sung-Liang Chen, Zicheng Hu, Nana Liu, Yian Deng, Wei Hu, Cong Tan, Wenfeng Zhang, Mai Tuyet Nhi, Gregor Koehler, Rapheal Stock, Klaus Maier-Hein, Marawan Elbatel, Xiaomeng Li, Saad Slimani, Victor M Campello, Benard Ohene-Botwe, Isaac Khobo, Yuxin Huang, Zhenyan Han, Hongying Hou, Di Qiu, Zheng Zheng, Gongning Luo, Dong Ni, Yaosheng Lu, Karim Lekadir, Shuo Li | Medical Image Analysis, 104043, 2026
Quantum geometric-entropic optimization for customer lifetime value prediction: convergence theory and an empirical study on transactional retail data
2026 | Massimiliano Ferrara, Laura Sáez-Ortuño, Santiago Forgas-Coll, Jorge Refugio Fabila-Fabián, Carlos Martín-Isla, Karim Lekadir | Statistics, 1-25, 2026
Bridging gaps in youth mental health care: YOUTHreach—a comprehensive European strategy
2026 | Thérèse van Amelsvoort, Anouk Boonstra, Karim Lekadir, Esmeralda Ruiz Pujadas, Matthew R Broome, Sian Lowri Griffiths, Gary Donohoe, Arne Popma, Giovanni de Girolamo, Covadonga M Díaz-Caneja, Anna-Kaisa Oidermaa, Andreas Bechdolf, Silvia Evers, Ghislaine van Mastrigt, Dorothee Horstkötter, Ricky Janssen, Maria Bulgheroni, David McDaid, Jan R Boehnke, Patrick McGorry, Mario Alvarez Jimenez, Anita Schick, Ulrich Reininghaus, Sophie Leijdesdorff | European Child & Adolescent Psychiatry, 1-9, 2026
