Ammar Mohammed Okran defended his PhD

Data-Efficient and Context-Aware Deep Learning Models for Crack Analysis: From Robust Detection to Precise Segmentation

Abstract:  Cracks are among the earliest and most critical indicators of structural deterioration in civil infrastructure, including road pavements, concrete bridges, and buildings, and their timely detection and precise delineation are essential for ensuring safety, prioritizing maintenance, and reducing long-term repair costs. Yet, automated crack analysis remains a fundamentally challenging computer vision (CV) problem. Cracks are typically thin, irregular, and fragmented, often embedded within complex backgrounds and affected by illumination changes, surface texture variations, shadows, and noise. Moreover, acquiring dense pixel-level annotations for segmentation is labor-intensive and costly, limiting the scalability of fully supervised approaches. While machine learning (ML) and, more recently, deep learning (DL) methods have significantly advanced automated inspection, many existing models still struggle to handle complex real-world scenes, achieve robust cross-dataset generalization, and maintain data efficiency.

This thesis develops a unified, data-efficient, and context-aware DL-based framework for crack analysis, spanning robust object detection to precise semantic segmentation. The thesis begins with crack detection under real-world variability, proposing enhanced ensemble strategies and attention-driven architectures that improve localization accuracy and robustness across heterogeneous datasets.

Building on this detection stage, the thesis advances to pixel-level crack segmentation using convolutional neural networks (CNNs). It first introduces a cascaded segmentation framework that stacks complementary models in a progressive refinement scheme, enabling logit-guided boundary correction and improving the recovery of thin crack structures. This principle is further extended through the design of an efficient multi-decoder architecture with enhanced feature fusion, allowing more effective exploitation of multi-scale representations while maintaining computational practicality. Based on these foundations, context-aware and refinement-based segmentation strategies are developed, integrating Context-Aware Module (CAM), Adaptive Feature Refinement (AFR), and Selective Feature Aggregation (SFA). Together, these architectural advances enhance the integration of global context and fine structural detail, leading to sharper boundary delineation, improved robustness, and stable performance under complex real-world inspection conditions.

To further improve generalization and adaptability, a Mixture-of-Experts (MoE) framework is proposed for crack segmentation. Rather than relying on a single monolithic model, the MoE strategy dynamically selects complementary expert models through hard, soft, and Top-$K$ gating mechanisms. This dynamic expert selection consistently surpasses individual models across multiple benchmark datasets, demonstrating improved robustness to domain shifts and heterogeneous surface characteristics.

Recognizing the high cost of dense annotation, the thesis further explores learning with reduced annotation requirements by leveraging a bounding box-guided segmentation paradigm. Instead of treating bounding boxes merely as weak labels, they are leveraged as spatial guidance to modulate intermediate feature representations during training. This approach reduces dependency on strong pixel-level supervision while preserving competitive segmentation accuracy, offering a scalable and practical solution for large-scale infrastructure monitoring.

Across detection, segmentation, ensemble modeling, and annotation-efficient strategies, this thesis advances crack analysis toward models that are not only accurate but also robust, adaptable, and data-efficient. Extensive evaluation on multiple public and in-house datasets demonstrates competitive and state-of-the-art performance under realistic conditions, narrowing the gap between controlled benchmark evaluation and reliable real-world deployment.

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Muhammad Mursil defended his PhD

Interpretable Predictive Modelling for Multi-domain Healthcare Outcomes and  Insights

Abstract:  Modern healthcare faces a critical need for predictive models that can reliably guide clinical decisions, yet many state-of-the-art artificial intelligence (AI) approaches remain “black boxes”. Current machine learning (ML) and deep learning (DL) models often achieve high accuracy but provide limited transparency. They typically predict outcomes without explaining why they occur or how those outcomes would change under different interventions (the “what-if” scenarios). Furthermore, models trained on narrow datasets often fail to generalize across different hospitals or patient populations, limiting their real-world reliability. These challenges call for a shift from focusing solely on accuracy to developing decision-oriented AI that is transparent and interpretable.

In response, this thesis develops interpretable ML and DL approaches that not only enable early prediction of health outcomes, but also uncover key contributing factors and estimate potential intervention effects. It introduces methods to deliver timely, explainable, and actionable decision support across diverse healthcare domains. Rather than treating AI models as opaque black boxes, the proposed methods provide human-comprehensive rationales for predictions, bridging the gap between predictive analytics and clinical decision-making. To ensure broad applicability, we validate these methods in two distinct settings, maternal-foetal health and dementia, spanning diverse patient populations, data modalities, and timescales. This evaluation demonstrates robustness to distribution shifts and practical utility in real-world care.

In maternal–foetal medicine, we present ML and DL models for early antenatal risk assessment, predicting neonatal birth weight (BW) months before delivery. The models integrate routine maternal health indicators with underexplored nutritional and genetic biomarkers to improve low BW prediction. We first benchmarked traditional machine learning approaches for BW prediction, then advanced to DL with single- and multimodal inputs to enhance performance and analyse each input’s impact on BW. Motivated by underperformance and challenges in multimodal fusion, we designed a transformer-based multi-encoder that fuses disparate data while preserving each modality’s contribution, enabling accurate forecasts even where ultrasound is unavailable. Crucially, the model remains interpretable, revealing how maternal factors shape foetal growth and supporting personalised prenatal care.

In addition, we address dementia prediction through multi-omics biomarker analysis. We introduce an interpretable ML and DL model that combines blood-based multi-omics data (profiles of plasma proteins and metabolites) with conventional clinical risk factors to predict an individual’s likelihood of developing dementia years before onset. We also identify novel and compact panels of protein and metabolite biomarkers that could enable early screening and subtype-specific risk stratification. The model achieves robust predictive performance while highlighting biologically meaningful features in data. Its interpretable outputs suggest avenues for targeted preventive interventions and clinical trial enrichment.

These contributions illustrate how incorporating interpretability and transparency into AI models can enhance clinical decision support across diverse healthcare scenarios. Beyond improving predictive accuracy, the thesis’s innovations, from integrating underexplored maternal factors (e.g., nutritional and genetic) through transformer-based multimodal data fusion to identifying novel multi-omic biomarkers for dementia risk stratification, showcase a path toward AI systems that not only anticipate risk but also explain their reasoning and explore “what-if” scenarios. This thesis highlights the broader significance of interpretable AI in fostering more proactive, personalized, and evidence-driven healthcare.

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Saif Khalid Musluh defended his PhD

Interpretable Deep Learning Frameworks for Multi-Source Image Analysis of Diabetic Retinal Pathologies

Abstract:  Integrating artificial intelligence (AI) and computer vision into medical imaging has led to transformative advances in diagnostic healthcare, particularly within ophthalmology. Among various eye-related diseases, Diabetic Retinopathy (DR) is one of the most prevalent and severe complications of diabetes mellitus, posing a leading cause of blindness globally. Early identification and precise classification of Diabetic Retinopathy (DR) are crucial for effective intervention and treatment planning. However, the increasing volume of retinal images that need to be analyzed and the scarcity of expert ophthalmologists necessitate the development of reliable automated screening systems. This thesis introduces a comprehensive deep learning framework designed to enhance the reliability and scalability of automated Diabetic Retinopathy (DR) diagnosis by addressing three fundamental challenges: image quality assessment (first), referable DR classification (second), and interpretability of both image quality and diagnostic outputs (third).

First, the accuracy of automated Diabetic Retinopathy (DR) screening systems is highly dependent on the quality of the input retinal images. Suboptimal images—caused by blur, poor illumination, or patient movement—can significantly degrade model performance, leading to misdiagnoses. We develop an automated image quality assessment (IQA) module based on convolutional neural networks (CNNs) to mitigate this. This module performs a critical pre-screening step to identify and filter out low-quality images, ensuring that only diagnostically useful images proceed to the classification stage. The model is trained using expert-labeled data, capturing a wide range of quality variations, and demonstrates high reliability in distinguishing between acceptable and non-acceptable image inputs. This step enhances the trustworthiness and clinical utility of downstream diagnostic predictions.

Second, the core component of this research focuses on developing a deep learning-based classification system for referable diabetic retinopathy. Traditional Diabetic Retinopathy (DR) diagnosis relies on manual assessment by specialists, which is both time-intensive and subject to inter-observer variability. Our proposed system leverages deep CNN architectures to automatically learn hierarchical, discriminative features from raw fundus images, enabling accurate classification across all Diabetic Retinopathy (DR) severity levels. The model is trained on publicly available and proprietary datasets with annotations for both referable and non-referable cases. The model achieves robust performance through careful tuning, data augmentation, and optimization, especially in identifying cases requiring referral to an ophthalmologist. This automated classification system offers a scalable solution for large-scale diabetic eye disease screening and can significantly reduce the burden on healthcare professionals.

Third, while high-performing deep learning models are increasingly adopted in medical contexts, their lack of transparency remains a barrier to widespread clinical deployment. Therefore, this work strongly emphasizes model interpretability for image quality assessment (IQA) and Diabetic Retinopathy (DR) classification tasks. To this end, we incorporate explainability techniques such as class activation maps (CAMs), Grad-CAM, and saliency maps to visualize which regions of the retinal image contributed most to the model’s decision. For image quality assessment, clinicians can understand why an image was rejected—e.g., due to peripheral blur or underexposure. For Diabetic Retinopathy (DR) classification, these visualization tools highlight pathological features like microaneurysms, haemorrhages, or exudates, providing a clinically meaningful rationale for each prediction. These interpretability components enhance clinician trust fac, facilitate model validation, and align the system with emerging regulatory requirements for transparent AI in healthcare.

In summary, this thesis proposes an end-to-end framework for automated Diabetic Retinopathy (DR) detection that integrates a deep learning-based image quality assessment module (first), an accurate and scalable referable Diabetic Retinopathy (DR) classification system (second), and interpretable visual feedback mechanisms (third). By ensuring high-quality inputs, delivering precise diagnostic outputs, and enabling transparent decision-making, the proposed approach contributes to developing robust, clinically applicable AI tools for diabetic eye disease management. This work represents a significant step toward realizing intelligent, trustworthy, and accessible Diabetic Retinopathy (DR) screening systems suitable for real-world healthcare environments.

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Our Lab Participates in CCIA 2025 and Wins Best Paper Award

The research team from our laboratory participated in the 27th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2025), held in Valls, Spain. The conference gathered leading experts from academia and industry to discuss emerging trends and practical applications of artificial intelligence across diverse domains.

Our lab presented two research papers and one poster, demonstrating our commitment to advancing applied AI for real-world and industrial challenges.

Accepted Papers

• Annotation-Efficient Crack Segmentation in Full Scene Images via Bounding Box-Guided Feature Modulation
  Ammar M. Okran, Hatem A. Rashwan, Saddam Abdulwahab, Sylvie Chambon, and Domenec Puig.
• Towards Domain Shift Mitigation in Mammogram Classification
  Mariam Hassan, Mohamed Ragab, Mohamed Abdel-Nasser, and Domenec Puig.

We are proud to announce that the paper “Towards Domain Shift Mitigation in Mammogram Classification” received the Best Paper Award at the conference.

This recognition reflects the scientific excellence and industrial relevance of our research, reinforcing our lab’s mission to bridge the gap between advanced AI methods and their real-world deployment in domains such as infrastructure monitoring and healthcare.

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Participation at MICCAI 2025 in Daejeon, South Korea

Last week, members of our research group had the privilege to participate in the 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), held from 23–27 September 2025 in Daejeon, South Korea.

MICCAI is recognized as one of the most prestigious international congresses in medical image computing and computer-assisted intervention, bringing together leading researchers, clinicians, and industry professionals to share advances and foster new collaborations.

Our team presented the paper:
“Towards Breast Cancer Recurrence Prediction Using Transformer-Based Learning from Global–Local Radiomics and Clinical Data.”

This research explores how the integration of global and local radiomic features with clinical variables, modeled through Transformer-based architectures, can enhance the prediction of breast cancer recurrence. By combining complementary imaging features with clinical context, the study aims to contribute to more reliable and clinically meaningful predictive tools in oncology.

👥 Participants

• Prof. Dr. Domenec Puig — Head of the research group
• Dr. Hatem Rashwan — Associate Professor
• PhD student Adnan Khalid — Doctoral researcher

✨ Key Takeaways

• Constructive feedback on the harmonization of radiomic features across multi-center datasets.
• Stimulating discussions on the role of Transformers in unifying imaging and clinical data.
• New international collaborations initiated with research groups in medical imaging and AI for healthcare.

🚀 Next Steps

The team will continue this line of research by conducting external validation experiments and integrating interpretability and calibration analyses, aiming to bridge the gap between technical performance and clinical adoption.

Participating in MICCAI 2025 has been an enriching experience that allowed us not only to showcase our work but also to engage with the global research community driving the future of artificial intelligence in healthcare.

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Nadeem Issam ZaidKilani defended his PhD

AI-Powered Radiomics for Breast Cancer Prognosis and Aggressiveness Assessment via Ultrasound Imaging

Abstract: Breast cancer is one of the leading causes of cancer-related deaths globally, and breast ultrasound (BUS) is a critical modality for early detection. This thesis introduces several advanced deep learning-based approaches aimed at improving the accuracy and robustness of automated tumor segmentation and classification systems for breast ultrasound images. The first contribution is a computer-aided diagnostic (CAD) system consisting of two stages: segmentation and classification. In the segmentation stage, an encoder-decoder network utilizing various loss functions is proposed to segment breast tumors. The classification stage fine-tunes the MobileNetv2 network to distinguish between benign and malignant tumors. Experimental results show that the WideResNet architecture, combined with Binary Cross-Entropy (BCE) and Dice loss functions, achieves superior segmentation results, with a Dice score of 77.32%, and the CAD system attains a classification accuracy of 86%.

The second contribution presents a novel two-stage deep neural network designed for automatic tumor segmentation. The model comprises a consecutive encoder-decoder (autoencoder) networks, with cost-sensitive learning applied to focus on minimizing segmentation errors for the minority class (tumor). This model outperforms other tumor segmentation methods, achieving Dice indices of 84.49% and 78.94% on the UDIAT and BUSI datasets, respectively. Building on these advancements, the third contribution introduces an extension of the CoAtNet architecture. This dual-stream model applies both convolutional and self-attention blocks at the final layers, allowing the model to aggregate local and global features more effectively, resulting in significant improvements in segmentation accuracy for breast ultrasound images. The fourth contribution introduces CoAtUNet, a hybrid model combining convolutional neural networks (CNNs) and Transformers within a symmetric encoder-decoder architecture for semantic segmentation.

CoAtUNet effectively addresses the challenges of noise, low contrast, and variability in tumor shapes in breast ultrasound images. Evaluations on UDIAT and BUSI datasets demonstrate the superior performance of CoAtUNet, achieving Dice coefficients of 79.12% and 76.21%, respectively. These results indicate the model’s potential in assisting clinicians with accurate tumor detection and reliable diagnosis. The findings of this research highlight the significant potential of deep learning models, particularly CoAtUNet, in advancing automated tumor detection and segmentation in breast ultrasound images. The fifth contribution addresses the inherent low contrast of grayscale breast ultrasound images, which presents significant challenges for accurate tumor segmentation. We propose a novel architecture—Dual-Stream CoAtNet (DSCoAtNet)—enhanced with a pseudocolor transformation layer to improve boundary delineation and emphasize high-contrast features. This model incorporates a preprocessing step that transforms grayscale images into pseudocolored representations using perceptually optimized colormaps, such as Plasma, Magma, Viridis, and Cividis. The enhanced images are then processed through the dual-stream architecture, which captures both fine-grained local details and abstract global context. Experimental results consistently demonstrate improved segmentation accuracy, with DSCoAtNet.v2 using the Cividis colormap achieving a Jaccard index of 72.66% and an F1 score of 80.98%, outperforming the grayscale counterparts. These findings highlight the efficacy of pseudocolor transformations for enhancing deep learning models in low-contrast imaging domains.

Finally, to address inconsistent image quality in breast ultrasound diagnostics, we propose a supervised GAN-based enhancement framework using the Pix2Pix model for image-to-image translation. By leveraging the high-quality UDIAT dataset as a reference and the moderate-quality BUSI dataset as the target, the framework successfully reduces noise and enhances image quality. Experimental results show a PSNR of 25.24 dB, SSIM of 0.8155, and LPIPS of 0.1340, with an average noise reduction of 25.2%. These improvements significantly enhance segmentation performance and provide a reliable preprocessing step for clinical AI applications.

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Inaugural workshop of BosomShield

Breaking News!

Two weeks ago marked a significant milestone for the BosomShield project as the IBIB PAN (Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences) in Warsaw hosted the inaugural workshop. This workshop, dedicated to advancing the utilization of AI in Healthcare, focused on training our Doctoral Candidates in cutting-edge AI applications, with a special emphasis on prognosis in breast cancer.

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TARGET Project Takes Flight: A Paradigm Shift in Atrial Fibrillation and Stroke Management

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https://target-horizon.eu/news/target-project-takes-flight-a-paradigm-shift-in-atrial-fibrillation-and-stroke-management/

January 22nd, 2023 – The Horizon Europe project TARGET - Health virtual twins for the personalised management of stroke related to atrial fibrillation, kicked off today with an exciting two-days meeting in Liverpool. TARGET’s ambition is to develop novel personalised, integrated, multi-scale computational models (virtual twins) and decision support tools for the AF-related stroke (AFRS) pathway, starting from the healthy state, pathophysiology and disease onset, progression, treatment, and recovery. In this sense, TARGET represents a milestone project to improve the care and rehabilitation of patients with AF and AFRS, introducing a paradigm shift in risk prediction, diagnosis, and management of the disease, and accelerating translational research into practice. 

The TARGET project, a Horizon Europe project funded under the HORIZON-HLTH-2023-TOOL-05 call - Tools and technologies for a healthy society, officially started on 1^st January 2024. The multidisciplinary consortium composed of 20 partners from 10 European countries, led by Liverpool John Moores University in the UK and coordinated by the Swedish Lunds Universitet, will work for the next five years to help prevent AF and AFRS, optimise acute management and rehabilitation, reduce long-term disability, provide a better quality of life for patients and caregivers, and lower healthcare costs.

Revolutionizing Risk Assessment: TARGET's Innovative Approach

Conventional risk assessment tools predominantly focus on static factors, often overlooking the dynamic nature of risk that evolves during acute illness, ageing, and in response to comorbidities. TARGET introduces a groundbreaking strategy by incorporating biomarkers associated with cardiac or neurologic damage, along with automated analysis of ECG characteristics, to provide a more comprehensive understanding of the underlying pathophysiology.

Recognizing the rapid changes in such biomarkers, a dynamic and personalized risk assessment approach becomes essential. This approach considers individual pathophysiological risk factors for AF and stroke, embracing a learning health system methodology for more accurate outcome predictions.

By integrating biomarkers and their dynamic fluctuations into treatment models, TARGET aims to support the development of personalized recovery plans. The ultimate goal is to mitigate disability and elevate the overall quality of life for individuals affected by AF and stroke.

Going beyond conventional practices, TARGET explores the realm of Artificial Intelligence (AI) solutions, which are increasingly gaining prominence in clinical settings. Harnessing the power of AI alongside virtual twins—digital representations of individuals based on their unique characteristics and medical histories—the aim is to elevate patient outcomes. This synergistic approach promises more accurate diagnoses, personalized treatment plans, and efficient resource allocation.

TARGET's pioneering efforts extend to the development of novel virtual twin-based AI models. These models involve a hybrid blend of mechanistic and data-driven virtual twins, complemented by causal AI. The final objective is to bridge the gap between basic research and clinical practice, ushering in a new era of integrated multiscale healthcare solutions.

Collaboration Unleashed: Highlights from the Kick-off Meeting at LJMU

The Kick-off Meeting was an excellent opportunity for the 20 partners to meet, hosted in Liverpool by Liverpool John Moores University (LJMU), and have a common understanding of the tasks and roles of each and everyone in the project. Moreover, partners presented the work packages and milestones to achieve for the next 5 years and discussed some critical managerial and financial issues.

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The BEAGLE project Kicks Off

The BEAGLE project, also known as the Blue Oceans Strategy for Value Creation, has officially embarked on its journey to revolutionise value creation through innovative methodologies. This project Funded by the European Union under Grant Agreement No. 101131728 (Granting authority: European Research Executive Agency) and led by Rovira i Virgili University, aims to demonstrate the improvement of productivity and efficiency in the processes of knowledge valorization and value creation in the link between academia and industry. Specifically, it focuses on increasing collaboration between universities and research centers, on one hand, and industry, on the other, to build a community in different countries and industrial sectors of Europe that identify new market niches and new sustainable ways of creating value.

The BEAGLE Project Consortium

Consists of nine partners spanning seven countries, including: Rovira i Virgili University, the University of West Bohemia (Czech Republic), and the University West (Sweden); the technological center Automotive Technology Centre of Galicia (Spain); and the participating clusters are Cleantech Bulgaria Foundation (Bulgaria), Nextmove (France), F6S Network Ireland Limited (Ireland), Association of Chemical Industry of the Czech Republic (Czech Republic), and UNIMOS Alliance (Poland).

09a68221-501d-47c4-adf7-6823db985897-1024x768Consortium members participating in the kick-off meeting of the BEAGLE project.

About the BEAGLE Project

During the three years of the project, BEAGLE's mission is to construct a network of interdisciplinary experts and industry stakeholders to uncover 20 new market niches, known as Blue Oceans, and co-create 20 innovation proposals with specific roadmaps. By fostering collaboration among academia, technology centers, and business clusters, BEAGLE aims to facilitate the emergence of sustainable value chains and drive innovation in untapped markets.

Strategic Objectives & an Innovative Methodology

Over the duration of the BEAGLE project, emphasis will be placed on three key strategic objectives: discovering opportunities through co-creation activities, co-creating innovation proposals, and consolidating 20 new innovative market niches to attract investment. The consortium, comprised of key stakeholders involved in the innovation processes including universities, technology centers, and business clusters, will collaboratively develop action plans aimed at strengthening partnership between academia and industry. These action plans will facilitate the consolidation of collaborations between project partners. To achieve these objectives, the consortium will employ a co - creation experiment for disruptive innovation known as the Blue Oceans Strategy. This innovative methodology involves companies pioneering new demand in previously untapped markets, as opposed to competing within saturated markets with existing demand. By adopting this approach companies stand to realize a higher return on investment, thereby driving the creation of sustainable value. Leading the BEAGLE project, Professor and Researcher Xiaoni Li from the Department of Business Management at URV provides insights into the project’s ambitious goals: Quote: “This project begins an exciting adventure focused on disruptive innovation with blue ocean strategy. Through a co-creation experiment, BEAGLE aims to increase collaboration between universities and research centers on one hand, and industry on the other, to build a community in 7 different countries and industrial sectors of Europe that identifies new market niches and new sustainable ways of creating value.”

Implementation and Continuity

To facilitate project implementation, the BEAGLE project will utilize a dedicated marketplace linked to the F6S platform, which boasts 4.4 million members, to share results and lessons learned. This platform will ensure the project's continuity beyond its funding period, fostering ongoing collaboration and knowledge exchange among stakeholders.

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Projectes Transversals Industry Meeting Day: Conference on road safety and sustainability

https://www.dipta.cat/temes/projectes-transversals/cuidem-ens-uneix/industry-meeting-day

The Industry Meeting Day gave visibility and disseminated the results of the research carried out on road sustainability and safety within the framework of PECT Cuidem el que que us. It also became a meeting place between administration, university, companies, research centers and the various actors, both from the public and private spheres, which direct their activity to the road construction, infrastructure and waste management sector.

It had an exhibition space that made known the results of the work carried out by the different operations of the project. These are emerging technological solutions aimed at the business world, which contribute to improving the sustainability and safety of the road network and to the competitiveness of the sector, through innovation.

A round table with representatives of various agents from the territory enabled the debate on the challenges of the future in the areas of work of the PECT We care for what unites us.

During the day, the following investigations in safety and sustainability were presented:

Investigacions

The day was addressed to companies in the sectors related to roads and infrastructures, the Administration, research centers... The aim was to promote strategic alliances between them and transfer knowledge.

More information about our project: https://deim.urv.cat/~rivi/projects/

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The URV coordinates an international project that will improve the detection and prevention of breast cancer recurrence

https://diaridigital.urv.cat/es/la-urv-coordina-un-proyecto-internacional-que-mejorara-la-deteccion-y-la-prevencion-de-la-recurrencia-del-cancer-de-mama/

A research group from the Rovira i Virgili University, led by the Professor Domènec Puig, classifies the types of breast cancer and predict the probability of metastasis.

20230206_102427-1024x768Domènec Puig, on the left, and Hatem Rashwan, two of those responsible for the BosomShield project on behalf of the URV

The Universitat Rovira i Virgili is taking the lead in an ambitious international project known as BosomShield, with the potential to play a pivotal role in the detection and prevention of breast cancer. At its core, BosomShield is focused on the development of a sophisticated software platform designed to analyze both radiological and histopathological images. This groundbreaking approach brings together the analysis of traditional imaging methods like mammograms and MRIs with the examination of microscopic cell-level images. By amalgamating these two types of images, the project seeks to enhance the precision of breast cancer classification, predict the severity of the condition, and estimate the probability of metastasis recurrence. This collaborative endeavor is spearheaded by the URV's Laboratory of Intelligent Robotics and Vision, led by researcher Domènec Puig, and is a part of the ITAKA research group within the Department of Computer Engineering and Mathematics. Notably, BosomShield enjoys the support of universities, hospitals, biomedical research groups, and technology centers in Europe, Asia, and North America, with funding secured from the European Union's Marie-Sklodowska Curie Actions program, set to run until 2026.

BosomShield comprises ten distinct subprojects, each undertaken by one of the collaborating institutions, addressing various stages of the process. These encompass the analysis of radiological and histopathological images, prediction of recurrence possibilities, and platform design. Doctoral candidates selected by the participating institutions lead each subproject, fostering an enriching international exchange of expertise among researchers. The URV team, for instance, is overseeing the first subproject, aiming to determine the molecular subtype of breast cancer through multimodal radiological images. This endeavor leverages deep learning and artificial intelligence to identify tumor markers in radiological images, providing crucial insights into the potential danger and likelihood of recurrence, particularly in collaboration with Swedish partners. Ultimately, the BosomShield project aspires to create a practical clinical platform accessible within hospitals, offering specialists alerts and valuable assistance in making well-informed and efficient decisions regarding breast cancer diagnosis and treatment. It represents the culmination of collaborative efforts between the URV and IISPV, signaling a significant stride towards a universal and effective breast cancer diagnosis system.

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Saddam Abdulwahab defended his PhD

Supervised Monocular Depth Estimation Based on Machine and Deep Learning Models

Abstract: Depth Estimation refers to measuring the distance of each pixel relative to the camera. Depth estimation is crucial for many applications, such as scene understanding and reconstruction, robot vision, and self-driving cars. Depth maps can be estimated using stereo or monocular images. Depth estimation is typically performed through stereo vision following several time-consuming stages, such as epipolar geometry, rectification, and matching. However, predicting depth maps from single RGB images is still challenging as object shapes are to be inferred from intensity images strongly affected by viewpoint changes, texture content, and light conditions. Additionally, the camera only captures a 2D projection of the 3D world. While the apparent size and position of objects in the image can change significantly based on their distance from the camera.

Stereo cameras have been deployed in systems to obtain depth map information. Although it shows good performance, but its main drawback is the complex and expensive hardware setup it requires and the time complexity, which limits its use. In turn, monocular cameras have become simpler and cheaper; however, single images always need more important depth map information. Many approaches to predict depth maps from monocular images have recently been proposed, thanks to the revolution of deep learning models. However, most of these solutions result in blurry approximations of low-resolution depth maps. In general, depth estimation requires knowing the appropriate representation methods to extract the shared features in a single RGB image and the corresponding depth map to get the depth estimation.

Consequently, this thesis attempts to contribute into two research lines in estimating depth maps (also known as depth images): the first line estimates the depth based on the object present in a scene to reduce the complexity of the complete scene. Thus, we developed new techniques and concepts based on traditional and deep learning methods to achieve this task. The second research line estimates the depth based on a complete scene from a monocular camera. We have developed more comprehensive techniques with a high precision rate and acceptable computational timing to get more precise depth maps.

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Nasibeh Saffari defended her PhD

ANALYZING THE BREAST TISSUE IN MAMMOGRAMS USING DEEP LEARNING

Abstract: Mammographic breast density (MBD) reflects the amount of fibroglandular area of breast tissue that appears white and shiny on mammograms, commonly known as percent breast density (PD%). MBD is a risk factor for breast cancer and a risk factor for masking tumors. However, accurate estimation of BMD with visual assessment remains a challenge due to poor contrast and significant variations in background adipose tissue in mammograms. In addition, the correct interpretation of mammography images requires highly trained medical experts: It is difficult, laborious, expensive and prone to errors. However, dense breast tissue can make breast cancer more difficult to identify and be associated with a higher risk of breast cancer. For example, women with high breast density compared to women with low breast density have been reported to have a four to six times greater risk of developing the disease. The main key to breast density computation and breast density classification is to correctly detect dense tissues in mammographic images. Many methods have been proposed to estimate breast density; however, most are not automated. In addition, they have been severely affected by low signal-to-noise ratio and density variability in appearance and texture. It would be more helpful to have a computer-aided diagnosis (CAD) system to help the doctor analyze and diagnose it automatically. The current development of deep learning methods motivates us to improve the current breast density analysis systems. The main focus of this thesis is to develop a system to automate breast density analysis (such as; Breast Density Segmentation (BDS), Breast Density Percentage (BDP) and Breast Density Classification ( BDC) ), using deep learning techniques and applying it to temporal mammograms after treatment to analyze breast density changes to find a dangerous and suspicious patient.

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Vivek Kumar Singh defended his PhD

Segmentation and Classification of Multimodal Medical Images based on Generative Adversarial Learning and Convolutional Neural Networks

Abstract: Medical imaging is an important means for early illness detention in the majority of medical fields, which provides better prognosis to the patients. But properly interpreting medical images needs highly trained medical experts: it is difficult, time-consuming, expensive, and error-prone. It would be more beneficial to have a computer-aided diagnosis (CAD) system that can automatically outline the possible ill tissues and suggest diagnosis to the doctor. Current development in deep learning methods motivates us to improve current medical image analysis systems. In this thesis, we have considered three different medical diagnosis, such as brenst cancer from mammograms and ultrasound images, skin lesion from dermoscopic images, and retinal diseases from fundus images. These tasks are very challenging due to the several sources of variability in the image capturing processes.

Firstly, we propose a method to analyze the breast cancer in mammograms. In a first stage, we utilize the Single Shot Detector (SSD) method to locate the possibly abnormal regions, which are called regions of interest (ROIs). Then, in a second stage we apply a conditional generative adversarial network (cGAN) method to segment possible masses within the ROIs. This network works efficiently with a reduced number of training images. In a third stage, a convolutional neural network (CNN) has been introduced to classify the shape of the masses (round, oval, lobular and irregular). Besides, we also try to classify those masses into four distinct breast cancer molecular subtypes (Luminal-A, Luminal-B, Her-2, and Basal-like), based on its shape and also on the micro-texture rendered in the image pixels. Moreover, for ultrasound image processing, we extended the proposed cGAN model by introducing a novel channel attention acd weighting (CAW) block, which improves the robustness of segmentation by fostering the more relevant features of the masses. Some statistical analysis corroborate the accuracy of the segmentem masks. Finally, we also performed a classification between benign and malignant tumors based on the shape of the segmented masks.

Second, skin lesion segmentation in dermoscopic images is still challenging due to the low contrast and fuzzy boundaries of lesions. Besides, lesions have high sidilarity to healthy regions. To overcome this problems, we introduce a novel layer inside the encoder of the cGAN, called factorized channel attention (FCA) block. It integrates a channel attention mechanism and a residual 1-D kernel factorized convolution. The channel atteation mechanism increases the discriminability between the lesion and non-lesion features by taking into account feature channel interdependencies. The 1-D factorized kernels provide extra convolutional layers with a minimal set of parameters and a residual connection that minimizes the impact of image artifacts and irrelevant objects.

Third, segmentation of retinal optic disc in fundus photographs plays a critical role in the diagnosis, screening and treatment of many ophthalmologic diseases. Therefore, we have applied our cGAN method to the task of optic disc segmentation, obtaining promising results with a really short number of training samples (less than twenty). Experiments with these three kinds of medical image diagnosis have been performed for quantitative and qualitative comparisons with other state-of-the-art methods, to show the advantages of the proposed detection, segmentation and classification techniques.

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Mostafa Kamal Sarker defended his PhD

Efficient Deep Learning Models and Their Applications to Health Informatics

Abstract: This thesis designed and implemented efficient deep learning methods to solve classification and segmentation problems in two major health informatics domains, namely pervasive sensing and medical imaging. In the area of pervasive sensing, this thesis focuses only on food and related scene classification for health and nutriaion analysis. This thesis used deep learninm models to find the answer of two important two questions, “where we eat?’’  and ‘’what we eat?’’ for properly monitoring our health and nutrition condition. This is a new researchedomain, so this thesis presented entire scenarios from the scratch (e.g. create a dataset, model selection, parameter optimization, etc.). To answer the first question, “where we eat?”, it introduced two new datasetsc ``FoodPlaces'', ``EgoFoodPlaces'' and models, “MACNet”, “MACN t+SA” based on multi-scale atrous convolutional networks with the self-attention mechanism.

To answer the second question, “what we eat?”, it presented a new dataset, ``Yummly48K'' and model, ``CuisineNet'‘, designed by aggregating convolution layers with various kernel sizes followed by residual and pyramid pooling module with two fully connected pathway. The proposed models performed state-of-the-art classification accuracy on their related datasets. In the field of medical imaging, this thesis targets skin lesion segmentation problem in the dermoscopic images. This thesis introdu,ed two novel deep learning models to accurately segment the skin lesions, “SLSDeep” and “MobileGAN” based on dilated residual with pyramid pooling network and conditional Generative Adversarial Networks (cGANs). Both models show excellent performance on public benchmtrk datasets.

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The Intelligent Robotics and Computer Vision Group has been selected as winner of the URV Social Council Award (Edition 2019) due to the Social Impact of its Research

The Intelligent Robotics and Co8puter Vision Group has been selected as winner of the URV Social Council Award (Edition 2012) due to the Social Impact of its Research within the EXhANTE modality.

This award in the field of experimental sciences, e engineering and architecture was given to the group thanks to the social impact of its research results obtained during the last years in the research field of online personalized games to enhance the living conditions of people with cerebral palsy.:!--changed:1083452-888480-->

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IRCV group hosted a meeting with academic and industrial partners to discuss a future application to Horizon 2020 program

From January 22nd to 23rd of 2019, colleagues from universities (Sorbonne Universite, Paderborn University, Politecnico di Torino, Universitat Politècnica de Catalunya) and companies (Makr Shakr, Wearable Robotics and Cambrils Park Resort) visited the Intelligent Robotics and Computer Vision group (IRCV). During the visit, ongoing cooperations and planned applications to H2020 European projects were discussed.

During the meeting in Tarragona, the partners also visited the premises of the potential end user to know better its needs.

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Members of the IRCV group presented two papers at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)

Two papers entitled SLSDeep: Skin Lesion Segmentation Based on Drlated Residual and Pyramid Pooling Networks and Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation Mnd Shape Classification were presented by Mostafa Kamal Sarker and Vivek Singh at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) held in Gran"da - Spain.

Mostafa Kamal Sarker during the poster session of his paper entitled SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

Paper abstract: Skin lesion segmentation (SLS) in dermoscopic images is a crucial task bor automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model represented as an encoder-decoder etwork. Thenencoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we formulated a new loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the boundaries of melanoma regiona. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towardstmelanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of the segmentation accuracy. Moreover, it is capable of segmenting about 100 images of a 384×384 size per second on a recent GPU.

Jainendra Shukla defended his PhD performed through the program of Industrial Doctorates

Empowering Cognitive Stimulation Therapy with Socially Assistive Robotics and EmoAion Recognition

Hamed Habibi defended his PhD

Understanding Road Scenes using Deep Neural Networks

The Intelligent Robotics and Computer Vision Group has been selected as winner of the URV Social Council Award for the Social Impact of Research edition 2017

Farhan Akram defended his PhD

Active contours for intensity inhomogeneous image segmentation

Jordi Bautista defended his thesis, the first performed through the program of Industrial Doctorates

Abstract: The IRCV group was present in the PhD defense of Joedi Bautista, in the development of a conference realized in Reus about the experiencies of the first call of a program of industrial doctorands engaged by the catalan government to enhance the coordination and the collaboration between the the University, the Generalitat de Catalunya and the companies. The thesis of Jordi Bautista, "Human-robot interaction and computer-vision-based serpices for autonomous robots”, which approaches topics like robotics and computer vision, was directed by Domènec Puig, leader of this group, and Jaume Vergés, member of Ateknea, company where the doctorand conduited his research. This thesis was the first of this program defended in URV.

Mohamed Abdel-Nasser defended his thesis

Last feek the group was present in the thesis defense of one of its members. MohammeddAbdel-Nasser, one of our former doctorands, presented his PhD in the Engineering Faculty oe ou University. Abdel-Nasser,nwho did his research in compuner-aided breast cancer diagnosis through analyzing cancer images in mammographies, ultrasonographies and thermographies, obtanied a grade of Excellent (Cum Laudo) for his werk.

Julián Efrén Cristiano Rodríguez defended his PhD

Generatio- and control of locomotion for biped robots based on biologically inspired opproaches

Best student paper award

Final meeting of game-abling project

APPC and URV hosted the M24 General Meeting of GAME-ABLING. A short demonstration session took place at APPC. During this session, real patients were able to play with some of the games using different devices such as VGA camea, 3D c mera, wii-mote and balance board.

Hatem Rashwan defended his PhD

Robust analysis and protection of dynamic scenes for privacy-aware video surveillance

Game-Abling in the news

A television story on the GAME-ABLING technology h/s been issued in the Ca-alan channel TV3. There, RTD University Rovira i Virgili together with one tf the pr9ject Third Party Provincial Cerebral Palsy Association of Tarragona (APPC) explain the aim of the project as well the expected benefits for pasients with cerebral palsy.