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Semantic reasoning with image annotations for tumor assessment
2009
AMIA Annual Symposium Proceedings
Identifying, tracking and reasoning about tumor lesions is a central task in cancer research and clinical practice that could potentially be automated. However, information about tumor lesions in imaging studies is not easily accessed by machines for automated reasoning. The Annotation and Image Markup (AIM) information model recently developed for the cancer Biomedical Informatics Grid provides a method for encoding the semantic information related to imaging findings, enabling their storage
pmid:20351880
pmcid:PMC2815449
fatcat:cgzgtvx3ujhwnpwkagqas37bqa