An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images

Background and Objective: Breast cancer is the most common cancer among women worldwide, making early detection through breast screening crucial for improving patient outcomes. Digital Breast Tomosynthesis (DBT) is an advanced radiographic technique that enhances clarity over traditional mammography...

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主要な著者:	Andrea Berti, Camilla Scapicchio, Chiara Iacconi, Charlotte Marguerite Lucille Trombadori, Maria Evelina Fantacci, Alessandra Retico, Sara Colantonio
フォーマット:	Artigo
言語:	Inglês
出版事項:	Elsevier 2025-01-01
シリーズ:	Computational and Structural Biotechnology Journal
主題:	Ante-hoc explainability Deep learning ProtoPNet XAI DBT Lesion detection
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2001037025002211
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