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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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Myths and legends about videogames – Dr. Dèbora Gil at “Maldita Twitchería”

The Computer Vision Center (CVC) continues its collaboration with Maldita Tecnología to put in context several concepts associated with Artificial Intel·ligence. In this occasion, it was the turn of Dr. Dèbora Gil, who explained in Maldita Twitcheria how videogames can help in medical and computer vision research.

First, Dr. Gil detailed to Naiara Bellio and Joselu Zafra that the technology which is behind the development of more and more realistic videogames, it is the same that the used by her research group to create the CVC lung GPS.

Also, she talked about “serious games”, videogames with a tremendous potential in the field, among others, of neurocognitive rehabilitation and in the aeronautic industry, as they are showing in the European-funded project E-PILOTS

And least, but not last, Dèbora pointed out how computer vision research is taking advantage of the enormous advances that the videogames industry are making in GPU technology, a specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of high quality images

You can watch Dèbora’s intervention in “Maldita Twitchería” here (in Spanish, from 43′ 15″)

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Conclusiones ciclo “Inteligencia Artificial, ética y participación ciudadana. CVC – Fundación “la Caixa”

Las conclusiones de la segunda edición del ciclo de diálogos ciudadanos “Inteligencia Artificial, ética y participación ciudadana” un proyecto de ciencia ciudadana organizado por el Centro de Visión por Computador, con la colaboración de la Fundación ”la Caixa”, han sido recogidas en un reportaje a doble página por la Vanguardia.

Para realizar dicho reportaje, la Vanguardia entrevistó al subdirector del CVC, el Dr. Fernando Vilariño.

Puedes leer el artículo completo aquí: IA ética – La Vanguardia – La Vanguardia

 

El ciclo “Inteligencia Artificial, ética y participación ciudadana“ busca reflexionar sobre el futuro que queremos como sociedad en relación a la tecnología y la Inteligencia Artificial y fomentar el diálogo y la transformación social. Se trata de uno de los proyectos innovadores seleccionados en la última convocatoria del Palau Macaya, que se desarrolló en el mismo Palau Macaya desde el 11 de noviembre de 2020 hasta el 18 de mayo de 2021.

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Artificial intelligence tool developed to monitor via satellite the destruction of buildings in wars

  • Researchers from Barcelona and California, led by the Institute of Economic Analysis (IAE-CSIC) and the UAB, and with the participation of the Computer Vision Center (CVC), have applied machine learning to detect the destruction of buildings by artillery using neural networks.
  • This automated method would make possible to monitor the destruction of a bellicose conflict, almost in real-time, aiming to improve humanitarian response.

This method, developed in a project co-led by the IAE-CSIC and the UAB, and with the participation of the CVC researcher Dr. Joan Serrat is based on neural networks that have been trained to detect in satellite images characteristics of heavy weapons (artillery and bombing) destructive attacks, such as the debris of collapsed buildings or the presence of bomb craters.

In the study, which results are published in the journal Proceedings of the National Academy of Sciences (PNAS), the scientists have applied this method to monitor the destruction of six of Syria’s main cities (Aleppo, Daraa, Deir-Ez-Zor, Hama, Homs, and Raqqa), plagued by an armed conflict for more than ten years. The results show that this method has a high efficiency at monitoring. “Our approach can be applied to any populated area as long as repeated high-resolution satellite imagery is available”, explained the authors.

Including the time factor

“An essential element of the development is that the neural network superimposes and compares successive images of the same place, contrasting them on a timeline that always includes a first image before the war. Another novelty is the incorporation of spatial and temporal information, in other words, information that gives context to the observation of destruction. In addition, the tool incorporates a novel method of image labeling: the system can make reasonable assumptions using the contextual information and train the algorithm with the destruction information around a building”, said the IAE-CSIC researcher Dr Hannes Mueller, lead author of the article

Automated methods must be able to detect destruction in a context where the vast majority of images do not appear to be of destruction. However, they often interpret buildings as demolished that actually are not, resulting in a high false-positive rate (FPR).

For even in Aleppo, a heavily war-torn city, only 2.8% of all images of populated areas contain a building that was confirmed as destroyed by the United Nations Operational Satellite Applications Program (UNOSAT) in September 2016, where they do a manual classification.

Low accuracy is “a very serious problem. Even in cities heavily hit by conflict, only 1% of buildings are destroyed. “Hence, their detection is like looking for a needle in a haystack. If we have false positives in the images, the margin of error shoots up quickly. In this case, 20% accuracy, for example, means that if an algorithm says something is destroyed, only 20% of what it says is actually destroyed,” continued the IAE-CSIC scientist.

The study demonstrates that the trained algorithm is able to identify damage in areas of Aleppo city that are not part of the UNOSAT analysis. It also provides evidence that this method can identify shelling in all six cities.

The results of this work are promising. They enable applications for the detection and even near real-time monitoring of destruction by war conflicts.

“Our method is particularly well suited to take advantage of the increasing availability of high-resolution imagery. We have estimated that human manual labeling of our entire dataset would cost approximately $200,000, and additional image repetitions would increase this cost almost proportionally. With an automated method such as ours, the benefits are numerous. More frequent imaging helps improve accuracy and the additional cost is small”, concluded Dr. Joan Serrat, CVC and UAB researcher

In the media

Un algoritmo permite monitorizar la destrucción que causan las guerras – Agencia EFE

Un algoritmo permite monitorizar la destrucción que causan las guerras– El Diario

 

Reference: Monitoring war destruction from space using machine learning. Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, Joan Serrat, PNAS June 8, 2021 118 (23) e2025400118; https://doi.org/10.1073/pnas.2025400118 

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Pau Rodríguez, CVC alumni, awarded by the BBVA Foundation

Dr. Pau Rodríguez has been awarded by the BBVA Foundation and the “Sociedad Científica Informática de España (SCIE) as one of the best young Spanish computer scientists.

Pau has been distinguished with this recognition due to his contributions in the area of machine learning applied to computer vision, which impact has been demonstrated through his publications in international journals and contributions to high-impact conferences, as well as many collaborations with leading research centers.

“For me, this award is a recognition of the effort doing during my years at the CVC. I think initiatives like this are very important to promote research in our country. In fact, I consider that more actions are required to attract and retain talent and to reduce our dependence on technology from other countries”, declared Dr. Rodríguez

Pau defended his PhD thesis “Towards Robust Neural Models for Fine-Grained Image Recognition” at the CVC in march 2019, and currently he is assistant professor at the UAB and researcher at Element AI in Quebec (Canada)

In the media:

Premiado un joven barcelonés que diseñó un sistema de inteligencia artificial para detectar el dolor – El Pais

 

More information (in Spanish): https://www.fbbva.es/noticias/fallados-los-premios-de-investigacion-sociedad-cientifica-informatica-de-espana-scie-fundacion-bbva/