Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.

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Bibliographic Details
Title: Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.
Authors: Scholler J; PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France., Mandache D; AQUYRE Bioscences-LLTech SAS, Paris, France.; Institut Pasteur, Bioimage Analysis Unit, Paris, France., Mathieu MC; Gustave Roussy Cancer Campus, Department of Medical Biology and Pathology, Villejuif, France., Lakhdar AB; SCM Bichat, Paris, France., Darche M; Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France., Monfort T; PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France., Boccara C; PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France., Olivo-Marin JC; Institut Pasteur, Bioimage Analysis Unit, Paris, France., Grieve K; Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France.; Quinze-Vingts National Eye Hospital, Paris, France., Meas-Yedid V; Institut Pasteur, Bioimage Analysis Unit, Paris, France., la Guillaume EBA; AQUYRE Bioscences-LLTech SAS, Paris, France., Thouvenin O; PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France.
Source: Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2023 May; Vol. 10 (3), pp. 034504. Date of Electronic Publication: 2023 Jun 01.
Publication Type: Journal Article
Journal Info: Publisher: Society of Photo-Optical Instrumentation Engineers Country of Publication: United States NLM ID: 101643461 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2329-4302 (Print) Linking ISSN: 23294302 NLM ISO Abbreviation: J Med Imaging (Bellingham) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2329-4302
DOI:10.1117/1.JMI.10.3.034504