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1
Explainability methods for machine learning systems for mul..:
, In:
Proceedings of the 13th ACM Multimedia Systems Conference
,
Storås, Andrea M.
;
Strümke, Inga
;
Riegler, Michael A.
. - p. 347-351 , 2022
Link:
https://dl.acm.org/doi/10.1145/3524273.3533925
RT T1
Proceedings of the 13th ACM Multimedia Systems Conference
: T1
Explainability methods for machine learning systems for multimodal medical datasets : research proposal
UL https://suche.suub.uni-bremen.de/peid=acm-3533925&Exemplar=1&LAN=DE A1 Storås, Andrea M. A1 Strümke, Inga A1 Riegler, Michael A. A1 Halvorsen, Pål PB ACM YR 2022 K1 explainable artificial intelligence K1 medical imaging K1 personalized medicine K1 Applied computing K1 Life and medical sciences K1 Health care information systems K1 Human-centered computing K1 Information systems K1 Information systems applications K1 Multimedia information systems K1 Computing methodologies K1 Artificial intelligence K1 Computer vision K1 Machine learning SP 347 OP 351 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3524273.3533925 DO https://dl.acm.org/doi/10.1145/3524273.3533925 SF ELIB - SuUB Bremen
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