La sessió va apropar al públic alguns dels avenços més recents en l’estudi del cervell humà i el paper que hi juga actualment la intel·ligència artificial. Durant la conferència, Casamitjana va explicar com les noves eines computacionals permeten connectar diferents nivells d’estudi del cervell, des de l’escala microscòpica de les cèl·lules fins a l’anàlisi macroscòpica dels sistemes cerebrals i la seva interrelació.

La conferència també va presentar el projecte NextBrain, una iniciativa de recerca que utilitza tècniques d’intel·ligència artificial per oferir un mapa complet de l’arquitectura cel·lular del cervell sense precedents. Les tècniques desenvolupades permeten una anàlisi ràpida, precisa i accessible d’imatges cerebrals en individus vius. Aquest enfoc permet identificar patrons cerebrals complexos, facilitant l’estudi del desenvolupament i l’envelliment cerebral, així com en l’impacte de diverses malalties neurodegeneratives. Totes les eines del projecte NextBrain han estat publicades lliurement a la comunitat científica per a futurs estudis: https://github-pages.ucl.ac.uk/NextBrain

Veiem, doncs, com la intel·ligència artificial esdevé una tecnologia crucial en l’estudi del cervell, ja que permet examinar grans quantitats de dades amb patrons complexes. Obre les portes a estudis fins ara inimaginables, integrant dades de múltiples fonts que anticipin la detecció de malalties cerebrals. Ens permetrà, per exemple, poder dissenyar protocols i intervencions personalitzades i, fins i tot, crear bessons digitals que permetin simular la evolució dels pacients i l’impacte dels tractaments.

El cicle “Últimes fronteres. La ciència al segle XXI”, organitzat per la Casa de Cultura de Girona, té com a objectiu apropar la recerca científica i tecnològica més actual a la ciutadania. La sessió es va gravar en vídeo i estarà disponible al canal de Youtube de la Casa de Cultura de Girona.

The applications presented span high-impact domains such as ecology (biodiversity estimation, tree species census, and biomass assessment) and cultural heritage preservation through the development of large-scale databases of historical documents.

The visit also included working meetings with the obstetrics team at Dr. Josep Trueta University Hospital, involving Dr. Anna Maroto and Dr. Clara Martínez, to explore ongoing research lines in fetal ultrasound imaging and identify new opportunities for clinical collaboration.

This activity is part of a long-standing collaboration between the two researchers, initiated in 2019 and consolidated through joint publications and competitive research proposals. In this context, it is also framed within the Ramón y Cajal programme awarded to Mariano Cabezas (RYC2023-042706-I), one of Spain’s flagship initiatives for the recruitment and consolidation of research talent, which supports his upcoming research stay in Japan and enables the expansion of these joint research lines. In parallel, Yago Díez’s involvement is linked to the J-PEAKS programme (2026 J-PEAKS Accelerated International Network Construction Support Program), aimed at fostering international research networks.

A Day of Collaboration and Learning

The day was thoughtfully structured to combine strategic planning with practical experience:

In the morning, partners gathered at UdiGitalEdu, the research group of the University of Girona coordinating the project, to:

• Establish project protocols ensuring smooth coordination and collaboration
• Plan dissemination activities to raise awareness and engage target groups, focusing on secondary multicultural schools
• Co-design tools for collecting data to perform a thorough current situation analysis, ensuring that project resources are based on solid evidence and informed decisions

In the afternoon, the group moved to the CIRS (Underwater Vision and Robotics Research Centre), where partners:

• Participated in capacity-building workshops on low-cost underwater robotics
• Explored innovative STEAM teaching methodologies and practical applications for marine science and technology education
• Shared knowledge and experiences regarding inclusive education principles and culturally responsive teaching, ensuring the DIVE outputs meet the needs of diverse student populations

Looking Ahead: Transforming STEM Education

Over the next three years, DIVE will:

• Develop an inclusive hands-on curriculum in the fields related to underwater robotics and marine science
• Train 260 teachers in innovative, interdisciplinary STEAM approaches
• Reach 4,000 students in multicultural classrooms
• Build a sustainable network of educators across Spain (Catalonia and Canary Islands), Greece, and Portugal to foster collaboration and knowledge exchange

The Girona kick-off provided a dynamic space for partners to connect, share expertise, and lay the groundwork for the project’s objectives.

Stay tuned as we dive into this exciting journey, shaping the future of STEAM education across Europe!

During the conference, Vicorob presented the poster “Volumetric-Guided Brain Image Synthesis with Diffusion Models”, showcasing a novel approach to generating and transforming brain MRI images using artificial intelligence.

The work introduced a method that can generate brain MRI images and transform existing ones. It allows adjusting specific brain regions to explore “what if” scenarios, such as simulating changes related to aging or neurological conditions, and can predict how brain structure may evolve over time. These capabilities provide a flexible tool for studying brain changes and supporting research in neuroscience and medical imaging.

The conference program included keynote talks by leading researchers such as Ellen Van Obberghen-Schilling, who presented advances in AI-driven spatial imaging for personalized tumor treatments; Tom Vercauteren, who explored AI-powered hyperspectral imaging for real-time surgical support; and Katrien Verbert, who discussed interactive and conversational AI to improve the understanding of AI-based decisions.

Vicorob’s participation in IABM 2026 reinforces its commitment to advancing artificial intelligence technologies with a strong focus on real-world biomedical applications and societal impact.

The workshop brought together all the research teams whose projects were selected in this call, focusing on robotics and digital technologies for sustainable agriculture. MULTIFUSE, a project with participation from Vicorob, is one of the six funded projects addressing how robotics and digital technologies can support more sustainable agricultural practices.

This first meeting provided a valuable opportunity to network with other international teams, exchange perspectives, and start shaping collaboration between European and Japanese partners.

Robotics and digital technologies for sustainable agriculture

On January 31, 2025, the EIG CONCERT-Japan initiative announced the six selected projects of its 11th joint call on digital transformations and robotics in sustainable agriculture.

MULTIFUSE: artificial intelligence for plant stress detection

The MULTIFUSE project focuses on advanced multimodal sensing and data fusion for early digital detection of plant stress symptoms.

By combining data from various optical sensors, the research trains artificial intelligence to detect plant stress, such as water deficiency or infection, at a physiological level before it becomes visible to the human eye.

Other projects in robotics and agriculture

The six selected projects address sustainable agriculture through robotics, artificial intelligence, and digital technologies:

• HydroNPK-AI focuses on AI-enhanced NPK management for optimized hydroponic cultivation. Using real-time artificial intelligence, the system manages nitrogen, phosphorus, and potassium in hydroponic setups to maximize yield while reducing chemical runoff.
• DRAGONS develops drones equipped with robotic arms to autonomously collect plant samples such as leaves or fruit from complex vineyard canopies.
• DEMETER builds adaptive pruning robots through simulation, artificial intelligence, and intuitive teleoperation, allowing human experts to remotely guide robotic systems.
• DYNA-GROW focuses on dynamic lighting management systems and sustainable energy solutions for smart crop production, integrating renewable energy with intelligent lighting.
• Supporting Digital Solutions develops a framework to support the demonstration of sustainable digital solutions for agriculture and enable collaboration between Japanese and European startups.

Social sustainability in agriculture

In both Japan and Europe, the average farmer is over 60 years old. These projects aim to make agriculture physically easier and more attractive to younger generations through robotics and digital technologies.

International collaboration in robotics and sustainable agriculture

The workshop in Bonn marked the beginning of collaboration between the selected teams, enabling them to connect, exchange perspectives, and start working together in the field of robotics and sustainable agriculture.

Vicorob’s participation in MULTIFUSE is part of this international effort to explore how robotics and digital technologies can support more sustainable agricultural practices.

Prof. Pere Ridao, Director of CIRS, and Dr. Roger Pi Roig are representing the team during the event.

Their participation is linked to the MEMLS laser project, supported by the AGAUR Producte programme. The project focuses on advancing underwater 3D laser scanning technologies aimed at improving high-resolution seabed mapping and inspection capabilities.

More information about the project can be found here.

The Vicorob team engaged with IQUA Robotics, a spin-off closely linked to the institute.

Abstract:

This PhD thesis focuses on the development of deep learning methods to enhance medical image analysis, with a particular emphasis on improving focus, efficiency and fairness in acute ischemic stroke (AIS). AIS is a cerebrovascular disease that occurs when a cerebral artery becomes occluded, interrupting blood flow to part of the brain. Rapid diagnosis and treatment of AIS are essential to preserving salvageable tissue and minimizing long-term disability. However, not all patients benefit equally from current therapies despite similar clinical and procedural characteristics. Accurately identifying patients likely to benefit remains a major clinical challenge. In this context, neuroimaging, particularly brain Computed Tomography Angiography (CTA), plays a central role in patient triage during the acute stroke phase. The rich anatomical and vascular information present in CTA offers a valuable but complex input for automated image analysis, motivating the use of deep learning to support clinical decision-making.

This thesis explores strategies to fully utilize CTA data using deep learning methods tailored for AIS. The first contribution proposes strategies to guide neural networks toward vascular structures extracted from CTA while retaining contextual parenchymal information. Several methods to combine vascular segmentations and CTA data are evaluated, including an attention-inspired mechanism designed to enhance the model’s focus on clinically relevant features for AIS due to large vessel occlusion (LVO) detection. The second contribution introduces Learnable 3D Pooling (L3P), a novel convolutional neural network-based module that compresses
3D medical images into 2D feature maps, enabling efficient, lightweight, and interpretable models. L3P-based architectures are validated across multiple tasks, including LVO detection in CTA, as well as brain age prediction from 3D T1-weighted MRI. In all cases, L3P maintains competitive performance compared to fully 3D networks, while significantly reducing computational demands and enhancing feature interpretability. The third contribution addresses the issue of hidden confounders in medical imaging pipelines. A controlled experimental framework is proposed to simulate and analyze the effects of confounding variables in clinically relevant classification tasks. Using large ensembles of bootstrapped models, measurable distributional patterns, such as inflated model performance, reduced performance variability and convergence of training and validation metrics, are identified as indicators of confounder-driven learning. This enables the development of a practical, unsupervised method to flag potential hidden biases, even when the nature of the confounder is unknown.

The proposed approaches are broadly applicable across medical imaging modalities and disease domains, with particular relevance to neuroimaging. Designed with generalizability in mind, these methods have demonstrated utility beyond stroke, highlighting their potential for diverse clinical contexts. Collectively, the contributions of this thesis advance the development of deep learning tools with efficiency, focus, and fairness.

The complete doctoral thesis can be consulted in the official PhD repository of the University of Girona (DUGi-doc):  https://dugi-doc.udg.edu

Over 30 teachers from Spain, Italy, Romania, Bulgaria, Ireland, and Greece came together to explore how Montessori-inspired methods and creative technologies can change how mathematics is taught and learned in primary and lower secondary schools, especially in multicultural classrooms.

Over three inspiring days, participants:

• Explored the first version of the MARIAN Lesson Plan Collection
• Tried out classroom activities with children and saw how hands-on and technology-based learning can help students understand mathematics better
• Discussed the benefits of using active, inclusive, and creative methods in teaching mathematics

After these first training sessions, participants will try different activities in their classrooms and share their experiences. Their feedback will help the consortium create the second version of the MARIAN Lesson Plan Collection, which will be discussed at a meeting in Sofia.

Next, participants will become teacher trainers, sharing the MARIAN approach by leading workshops in their own countries. They will also help update the MARIAN Lesson Plan Collection in several languages. The project’s goal is to introduce new, inclusive, and engaging ways of teaching mathematics in classrooms across Europe.

The project is coordinated by the Institute of Marine Sciences (ICM-CSIC) and focuses on understanding and monitoring recovery processes in newly established Mediterranean marine reserves, addressing one of the major challenges in marine science: achieving high temporal and spatial resolution in deep-sea ecosystem monitoring.

ViCOROB lead the SMART-ME TECH subproject, which focuses on advancing the autonomy and capabilities of the Girona Intervention Autonomous Underwater Vehicles (I-AUVs). Our research activities include:

• High-resolution seabed mapping
• Underwater manipulation for data retrieval and lander repositioning
• Autonomous detection and interaction with landers

At the core of SMART-ME is the creation of OBSEA-DEEP, one of the first distributed and intelligent deep-sea observatories in Spain. The observatory will be deployed at 400 metres depth on the upper slope of the Catalan margin, within a recently established no-take marine reserve between Blanes and Palamós. This strategic location will enable continuous monitoring across diverse habitats, providing unprecedented insights into deep-sea ecological dynamics in protected environments.

Unlike traditional cabled observatories, SMART-ME relies on a network of autonomous and cooperative robotic platforms, including multiparametric landers, autonomous underwater vehicles (AUVs), and acoustic tagging systems. Data transfer will be ensured through pop-up buoys and a data-muling strategy enabled by I-AUVs, while the landers will be autonomously repositioned on the seabed, allowing adaptive monitoring in spatially heterogeneous environments.

By combining robotics, artificial intelligence and ecology, SMART-ME lays the foundations for a new generation of distributed and intelligent deep-sea observatories, contributing to marine ecosystem conservation while reinforcing Spain’s leadership in deep-sea research and technology.

AI4SEA combines AI-based planning, machine learning and robotic manipulation to advance the development of autonomous underwater technologies under real-world marine conditions.

ViCOROB leads the AI4AUV subproject, Artificial Intelligence for AUV-based underwater habitat restoration, focused on developing a dual-arm Intervention Autonomous Underwater Vehicle (AUV) with embedded AI. This system will enable autonomous perception, decision-making and manipulation, integrating deep learning, reinforcement learning and edge computing techniques to allow the robot to operate independently in complex underwater environments.

AI4SEA is a multidisciplinary research project aimed at the autonomous mapping, monitoring and restoration of underwater habitats, with Posidonia oceanica meadows as the main use case. The project seeks to deliver intelligent and scalable solutions that contribute to the sustainable management of marine ecosystems.

The project consortium includes:

• ViCOROB – University of Girona, leading AI4AUV
• University of the Balearic Islands (UIB), with AI4MAP – Embedded AI for underwater habitat Mapping
• Universitat Politècnica de Catalunya (UPC), with AI4MON – Embedded AI for continuous MONitoring of underwater habitats, through the OBSEA Observatory
• Institute of Marine Sciences (ICM-CSIC), with AI4ECO – Scientific validation of the AI-driven methodology for Marine habitat Assessment and Restoration