An interpretable deep learning approach for lesion detection and segmentation on whole-body [18F]FDG PET/CT

dc.contributor.author	Dirks, Ine
dc.contributor.author	Keyaerts, Marleen
dc.contributor.author	Neyns, Bart
dc.contributor.author	Vandemeulebroucke, Jef
dc.contributor.imecauthor	Dirks, Ine
dc.contributor.imecauthor	Vandemeulebroucke, Jef
dc.contributor.orcidimec	Vandemeulebroucke, Jef::0000-0001-5714-3254
dc.date.accessioned	2024-08-29T10:21:15Z
dc.date.available	2024-06-15T17:25:03Z
dc.date.available	2024-08-29T10:21:15Z
dc.date.issued	2024
dc.identifier.doi	10.1117/12.3005815
dc.identifier.eisbn	978-1-5106-7157-7
dc.identifier.isbn	978-1-5106-7156-0
dc.identifier.issn	1605-7422
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/44036
dc.publisher	SPIE-INT SOC OPTICAL ENGINEERING
dc.source.beginpage	Art. 1292617
dc.source.conference	Conference on Medical Imaging - Image Processing
dc.source.conferencedate	FEB 19-22, 2024
dc.source.conferencelocation	San Diego
dc.source.journal	Proceedings of SPIE
dc.source.numberofpages	10
dc.source.volume	12926
dc.title	An interpretable deep learning approach for lesion detection and segmentation on whole-body [18F]FDG PET/CT
dc.type	Proceedings paper
dspace.entity.type	Publication
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