Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

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Bibliographic Details
Title: Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.
Authors: Monnin K; University of Lausanne, Lausanne, Switzerland. killian.monnin@chuv.ch.; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland. killian.monnin@chuv.ch., Jeltsch P; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Fernandes-Mendes L; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Cazzagon V; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Gulizia M; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Jreige M; University of Lausanne, Lausanne, Switzerland.; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Fraga Christinet M; University of Lausanne, Lausanne, Switzerland.; Department of Gastro-enterology, Lausanne University Hospital, Lausanne, Switzerland., Girardet R; Department of Radiology, South Metropolitan Health Service, Murdoch, Australia., Dromain C; University of Lausanne, Lausanne, Switzerland.; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Richiardi J; University of Lausanne, Lausanne, Switzerland.; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland., Vietti-Violi N; University of Lausanne, Lausanne, Switzerland.; Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland.
Source: Abdominal radiology (New York) [Abdom Radiol (NY)] 2026 Jun; Vol. 51 (6), pp. 2843-2856. Date of Electronic Publication: 2025 Nov 11.
Publication Type: Journal Article
Journal Info: Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2366-0058
DOI:10.1007/s00261-025-05249-4