The International AI Doctoral Academy (AIDA) is launched to become an international reference portal for AI PhD students and researchers

The Computer Vision Center (CVC) is one of the Research & Industry Members of the recently launched International AI Doctoral Academy (AIDA) which is aimed to attract PhD and postdoc talents in Europe through educational and training activities in the field of Artificial Intelligence.

AIDA is the first academy of its kind in Europe and internationally. Coordinated by the Aristotle University of Thessaloniki, it has now a total of 67 top AI Members, a good mix of 50 excellent European Universities and 17 Research Institutes and Companies. Its membership is excellent, its scope is pan-European/international and its aims are high to become world reference for AI PhD studies. It is remarkable that 67 leading European AI partners joined efforts with the 5 Horizon Europe ICT48 AI flagship projects to foster PhD education excellence in AI. Therefore, AIDA can indeed attain a critical mass to have a large impact on AI academic education, industry workforce upskilling and in addressing very important social challenges, which range from the fight against disinformation to the provision of a human-centered and trustworthy AI that serves not only European citizens, but the humanity in general.

AIDA can also ensure European strategic autonomy in such critical technology as AI, with huge potential socio-economic impact and to reinforce Europe’s assets in AI, by benefiting its world-class researcher community, son that it stays at the forefront of AI developments. It can form a common AI resource center and become a shared facility offering access to knowledge and expertise and attracting talented researchers. It indeed aims high at becoming a world reference point, creating an easy entry point to AI excellence in Europe.

AIDA was successfully launched on November 3rd, with the celebration of its first General Assembly. The CVC is collaborating with AIDA, being one of its Research & Industry members. The CVC has more than 40 doctoral students currently completing their PhDs and, since its founding in 1995, more than 130 theses have been defended. As one of the cornerstones of the CVC’s mission and vision is the generation of talent at the highest international level, we are really proud to be part of this initiative.

Visit AIDA’s website:

Course on “Literate models for Computer Vision: Combining vision, language and Reading”

Within the framework of AIDA’s educational and training activities, the CVC is organising the short course  “Literate models for computer vision: Combining vision, language and reading”.

Through this short interactive course, students will have a chance to reconcile with the state of the art in reading systems, especially scene text recognition, and explore how image text enables us to tackle new and exciting computer vision tasks such as fine-grained image classification, cross-modal retrieval, captioning and visual question answering.

  • Date & time: December 20th 2021, from 10 am to 5 pm
  • Place: Online
  • Language: English
  • Lecturers: CVC researchers Dr. Dimosthenis Karatzas, Dr. Ernest Valveny, Dr. Lluís Gómez, Andrés Mafla, Ali Biten, Ruben Perez and Sergi Garcia.
  • Audience: PhD an Msc students with basic deep learning knowledge.

More information and inscriptions:




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NewsPress Release

Computer Vision meets archaeology to detect near 10k Archaeological Tumuli in Galicia


Researchers from the Computer Vision Center and the Landscape Archaeology Research Group (GIAP) of the Catalan Institute of Classical Archaeology (ICAC) have developed a hybrid algorithm that combines Deep Learning and Machine Learning to improve the automatic detection of archaeological tumuli avoiding the inclusion of most false positives.

Archaeological tumuli are one of the most common types of archaeological sites and can be found across the globe. This is perhaps why many studies have attempted to develop methods for their automated detection. Their characteristic tumular shape has been the primary feature for their identification on the field and in LiDAR-based topographic data, which usually takes the form of Digital Terrain Models (DTMs).

The simple shape of mounds or tumuli is ideal for their detection using deep learning approaches. Deep learning detectors usually require large quantities of training data (in the order of thousands of examples) to be able to produce significant results. However, the homogenously semi-hemispherical shape of tumuli, allows the training of usable detectors with a much lower quantity of training data, reducing considerably the effort required to obtain it and the significant computational resources necessary to train a convolutional neural network (CNN) detector.

This type of features, however, present an important drawback. Their common, simple, and regular shape is similar to many other non-archaeological features and therefore studies implementing methods for mound detection in LiDAR-derived DTMs and other high-resolution datasets are characterised by a very large presence of false positives (objects incorrectly identified as mounds).

During the initial research, the researchers from the GIAP group of the Catalan Institute of Classical Archaeology, Iban Berganzo and Hèctor A. Orengo, located almost 9000 tumuli in Galicia. However, not all of these were actual tumuli as the automated detection results also included false positives. After initial data validation was performed in collaboration with Dr. Miguel Carrero (University College London & University of Santiago de Compostela, GEPN-AAT), Dr. João Fonte (University of Exeter) and Dr. Benito Vilas (University of Vigo) they realised that from the ca. 9000 detected objects only ca. 7600 corresponded to real archaeological mounds. Although, this was an excellent result, well below the percentage of false positives presented by similar studies, they thought they could improve the detection rate while decreasing the number of false positives.

For this reason, during the summer, GIAP researchers in collaboration with CVC researcher Dr. Felipe Lumbreras developed a new approach to reduce the number of false positives while increasing the detection rate. After analysing the nature of the detected false positives, they developed a hybrid approach that mixes classical machine learning and deep learning. The objective was to obtain a more precise definition of archaeological tumuli in which not just the shape but also the multispectral characteristics of the objects will be considered when looking for tumuli.

The fist results have been published at the Remote Sensing journal. In this article, researchers give more information on the data analyzed and the performance of this innovative computer-based automatic detection initiative.


The results that this new approach has produced are nothing less than spectacular:

  • The area covered is the largest (to the extent of our knowledge) in which archaeological DL approaches have ever been applied and it covers almost 30,000 km2
  • 10,527 objects have been detected of which approximately 9,422 correspond to archaeological tumuli (after careful visual validation with high resolution imagery and pending ground validation). That is, a 89.5% of the detected tumuli correspond to true positives.
  • We have only employed open source data in this research. However, the use of higher resolution data, in particular higher resolution satellite imagery instead of the Sentinel 2 (10m/px) images employed, would radically decrease the number of false positives reaching a success rate above 97%.
  • Code, sources and results (including validation) are freely available and the code is designed to be used in freely accessible cloud computing platforms Google Colaboratory and Earth Engine) so the lack of computational resources will not pose a problem for its application to other study areas (even very large ones).

This approach provides a way forward for the detection of tumuli avoiding the inclusion of most false positives. The algorithm can be applied in areas of the world where topographic data of enough resolution are available. Providing specific training data, this hybrid approach can also be used to detect other types of features where large number of false positives area an issue.

Link to the paper:


This research has received funding from multiple sources, that we would like to acknowledge here: Iban Berganzo’s PhD is funded with an Ayuda a Equipos de Investigación Científica of the Fundación BBVA for the Project DIASur. Hector A. Orengo is a Ramón y Cajal Fellow (RYC-2016-19637) of the Spanish Ministry of Science, Innovation and Universities. Felipe Lumbreras work is supported in part by the Spanish Ministry of Science and Innovation project BOSSS TIN2017-89723-P. Miguel Carrero and João Fonte are Marie Skłodowska-Curie Fellows (Grant Agreements 886793 and 794048 respectively). Some of the GPUs used in these experiments are a donation of Nvidia Hardware Grant Programme.

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Events & CommunityNews

Dr. Alicia Fornés, winner of the “Academic/Researcher” prize in the Dona TIC Awards 2021

The CVC researcher, Dr. Alicia Fornés received the Dona TIC award in the Academic/Researcher category. The awards ceremony, which is celebrated annually in commemoration of the Ada Lovelace Day, was held on October 13th at CosmoCaixa Barcelona and broadcasted on Youtube.

The Dona TIC awards, which are boosted by the Department of the Vice-Presidency, Digital Policies and Territory of the Government of Catalonia, have the dual purpose of recognizing and disseminating the role and talent of women and offering references to girls to encourage them to pursue STEM disciplines (science, technology, engineering and mathematics) and to assume leadership positions in the sector. The ceremony was chaired by the Vice President and Minister of Digital Policies and Territory, Jordi Puigneró, and organized with the collaboration of the Catalan Women’s Institute, the Barcelona Chamber of Commerce’s Women, Business and Economy Observatory the Tertúlia Digital association.

In this edition, out of 80 nominations submitted, 8 women and 2 initiatives were awarded in 10 different categories. In the case of Dr. Alicia Fornés, she received the prize in the category Academic/Researcher, which recognizes the trajectory of women devoted to training and / or research in the field of ICT.

Dr. Alicia Fornés is PhD in Computer Science and Senior Research Fellow at the Computer Vision Center and the Autonomous University of Barcelona. She has more than 100 publications in international conferences and journals and she has led numerous national and international research projects, with the aim of improving the automatic recognition of textual and graphic manuscripts, contributing to the preservation and dissemination of historical and cultural heritage. She is currently promoting the creation of the “Catalonia Time Machine” which aims to create a computer simulator to visualize thousands of years of Catalan history. On the other hand, her research activity also focuses on the application of ICT in the field of health. She also carries out activities to encourage STEM vocations among young people.

The Dona TIC Award recognizes Dr. Fornés’ outstanding contributions to the areas of computer vision and digital humanities. During her speech at the awards ceremony, Dr. Alicia Fornés encouraged girls who are interested in ICTs to choose this path and fight to increase the presence of women in this field. “The journey is difficult but the ICTs really deserve the effort”, concluded Dr. Fornés.

Congratulations Alicia for this well-deserved recognition!

More information about the Dona TIC Awards:

Watch the awards ceremony again:

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The Computer Vision Center, Eurecat and Universitat de Barcelona, together in the fight against COVID-19


Currently, with the PCR and antigen test, clinicians have really good tools for diagnosing the COVID-19 disease when the SARS-CoV-2 virus is active within the patient organism. However, these tests have two drawbacks: first, when patients suffer pulmonary infection, these tests do not provide precise information regarding its extension, and second, once the virus is eliminated by the immune system, the tests will be negative even if the patient is still affected by pneumonia induced by the original COVID-19 infection, a condition that can last for weeks.

Therefore, for getting a more complete perspective of the severity of the disease, clinicians have to use imaging techniques, as computed tomography (CT) scans. But, despite the increase in COVID-19’s detection accuracy through the use of CT images, the reading time necessary to interpret 3D CT volumes and to extract the morphological properties of the lesion can greatly increase the workload of radiologists. However, the use of Computer Vision and Artificial Intelligence tools can help to sensibly reduce the interpretation time.

With this objective in mind, Dr. Petia Radeva, CVC researcher and professor at the Universitat de Barcelona (UB), Giuseppe Pezzano and Drs Oliver Díaz and Vicent Ribas, from the Eurecat technology centre, have developed an automated method for COVID-19 detection using chest CT images, together with the segmentation of the Ground-Glass-Opacities (area of increased attenuation due to air displacement by fluid, airway collapse, fibrosis, or a neoplastic process) and other solidifications/fibrosis present inside the lungs, powered by deep learning strategies to support decision-making process. This study was recently published in the journal Computers in Biology and Medicine.

The procedure is simple: the lungs are firstly segmented from the input CT image to reduce the searching area. Afterward, the detection algorithm is used to analyse the lungs’ area in order to detect the presence of COVID-19. In the case of a positive finding, the CT image is processed to identify the areas affected by the disease.

This algorithm was tested with 79 COVID-19 CT volumes and 110 CT slices for three open-access COVID-19 CT image repositories, achieving an average accuracy for lesion segmentation near 99%. No false positives were observed in the detection network after 10 different runs.

The robustness and the accuracy of this work open up a wide range of other possible applications of this method. For example, the proposed network could be adapted, using fine-tuning, for studying the worst cases of pneumonia, the diffused metastasis or other lung diseases. Also, this methodology could be applied to detect and segment a large variety of organs in other fields of medical imaging analysis.


Reference: CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation. Giuseppe Pezzano, Oliver Díaz, Vicent Ribas and Petia Radeva, Computers in Biology and Medicine, September 2021, 136 104689.


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In the mediaNewsPress Release

Radiolung, a joint project of the Computer Vision Center and the Germans Trias Hospital, wins the Innovation Award from the Lung Ambition Alliance for lung cancer early detection

  • Radiolung is aimed to develop an artificial intelligence system based on radiomic information to improve the malignancy detection of lung nodules.
  • The research team is compound by members of the CVC research group Interactive Augmented Modeling for Biomedicine, led by Dr. Dèbora Gil, and various departments of the Hospital and Research Institute Germans Trias i Pujol, led by Dr. Antoni Rosell.

The Lung Ambition Alliance (LAA), a consortium recently created under the auspices of the International Association for the Study of Lung Cancer (IASLC), has recognized with the Innovation Award to the CVC and Germans Trias project “Radiomics and Radiogenomics in lung cancer screening – Radiolung”. The LAA Scientific Advisory Committee has distinguished the project led by Antoni Rosell, clinician of German Trias, for its scientific quality, novelty, potential application and contribution to the main goal of the alliance, double the lung cancer survival between 2020 and 2025.

Radiolung is aimed to develop a predictive model, based on artificial intelligence and radiomics, to improve the detection of malignancy of lung nodules and thus create a software with potential to be used with clinical purposes and improve the current model of lung cancer screening.

Radiomic for a more accurate diagnosis

Currently, lung cancer screening is performed by means of low-radiation computed tomography (CT-Chest) that detects lung nodules, but does not allow clinicians to assess, with enough accuracy, whether they are benign or malignant. Therefore, most patients have to undergo radiological follow-up, consisting of complementary radiological examinations and, sometimes, take of biopsy samples to refine the diagnosis.

In order to improve the diagnostic capacity of CT scans, researchers from the CVC and Germans Trias are working on an artificial intelligence algorithm that is able to analyse the mathematical characteristics of the images, a technique known as radiomics.

“Radiomics can extract a lot of 3D measurements of CT scans far beyond the visual capacity of the human eye, and combine them with histological and molecular characteristics of the lung tumours. In this way, you can systematically find, with a single CT image, the ranges that most correlate with the malignancy and/or severity of the nodule”, explains the principal investigator of the CVC research group Interactive Augmented Modeling for Biomedicine (IAM4B), Dr. Dèbora Gil. “This analysis of multiple data is impossible for a radiologist to perform it visually, so radiomics can be a very useful tool to help specialists to make more accurate diagnoses”, Dr. Gil continues.

For his part, Antoni Rosell, clinical director of the Thorax Area at Germans Trias and principal investigator of Radiolung, points out “being able to determine the degree of aggressiveness of the lung nodule and its mutational profile in its early stages by radiomics will allow to predict the long-term behaviour of the tumour, both in terms of evolution and the risk of recurrence. In this way, we will be able to provide targeted, specific and personalized treatments to patients, even in the initial state of the disease”.

With the Innovation Award, Radiolung has obtained 30.000 € for the development of this technology. “Thanks to this award, we will be able to transfer this innovation to the clinical practice,” concludes Rosell. The project has also been recognized by a Premi Talents grant, an initiative promoted by the Fundació Catalunya-La Pedrera and the Hospital Germans Trias, with the aim of funding research projects for health professionals who finished their residency.

In the media

Premian un proyecto de Can Ruti para la detección precoz del cáncer de pulmón – La Vanguardia (in spanish)

El projecte Radiolung de Germans Trias i del Centre de Visió per Computador guanya el Premi a la Innovació per a la detecció precoç de càncer – TOT Badalona (in catalan)

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