Methods and Benchmarks for Trustworthy AI in Breast Imaging
[eng] Breast cancer remains one of the most pressing global health challenges, with early detection and effective treatment planning relying heavily on advanced imaging and accurate interpretation. This PhD thesis addresses critical limitations in artificial intelligence (AI) applications for breast...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/227600 |
| Acceso en línea: | https://hdl.handle.net/2445/227600 http://hdl.handle.net/10803/696833 |
| Access Level: | acceso abierto |
| Palabra clave: | Intel·ligència artificial en medicina Diagnòstic per la imatge Mamografia Bases de dades en línia Medical artificial intelligence Diagnostic imaging Mammography Online databases |
| Sumario: | [eng] Breast cancer remains one of the most pressing global health challenges, with early detection and effective treatment planning relying heavily on advanced imaging and accurate interpretation. This PhD thesis addresses critical limitations in artificial intelligence (AI) applications for breast cancer imaging, specifically the lack of generalisability across clinical centres and the need for improved fairness across patient subgroups. The work focuses on two key modalities: digital mammography for early lesion detection, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for tumor segmentation and treatment response prediction. Four core contributions are presented: (1) a deep learning-based domain generalization framework for robust mass detection across unseen mammography domains; (2) a GAN-based augmentation method to improve detection in women with dense breast tissue, addressing a known fairness gap; (3) an image synthesis and domain adaptation strategy to harmonize MRI protocol variability and improve tumor segmentation; and (4) the development of a large-scale, multi-centre breast DCEMRI dataset with expert annotations and clinical outcome labels. These efforts culminated in the organization of the international MAMA-MIA Challenge (MICCAI 2025)—the first benchmark to evaluate breast MRI segmentation and treatment response prediction with integrated subgroup-aware fairness metrics. Together, these contributions demonstrate that targeted algorithmic innovation, inclusive data design, and open benchmarking can substantially enhance the robustness, equity, and translational potential of AI in breast cancer imaging. This thesis lays the foundation for next-generation decision support systems that are not only technically reliable, but also ethically aligned with the goals of personalized and equitable cancer care. |
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