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.

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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.” (more…)

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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%.

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