An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images
This study contributes to the growing field of AI in breast cancer screening by emphasizing the need for systems that are not only accurate but also transparent and interpretable. The proposed AI system marks a significant step forward in the timely and accurate analysis of DBT scans, with potential implications for improving early breast cancer detection and patient outcomes.
Authors
- Maria Evelina Fantacci (ORCID: https://orcid.org/0000-0003-2130-4372)
- Charlotte Marguerite Lucille Trombadori (ORCID: https://orcid.org/0000-0002-1238-9688)
- Chiara Iacconi (ORCID: https://orcid.org/0000-0002-0729-4166)
- Andrea Berti (ORCID: https://orcid.org/0000-0002-0089-6420)
- Camilla Scapicchio (ORCID: https://orcid.org/0000-0001-5984-0408)
- Alessandra Retico (ORCID: https://orcid.org/0000-0001-5135-4472)
- Sara Colantonio (ORCID: https://orcid.org/0000-0003-2022-0804)
Institutions
- University of Pisa (IT)
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- Agostino Gemelli University Polyclinic (IT)
- Accademia di Belle Arti di Carrara (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa (IT)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.06.008
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48