Publication | Open Access
Diagnostic captioning: a survey
30
Citations
76
References
2022
Year
EngineeringMachine LearningDiagnostic TextAbstract Diagnostic CaptioningCorpus LinguisticsNatural Language ProcessingMultimodal LlmLanguage DocumentationImage AnalysisData ScienceVisual GroundingComputational LinguisticsLanguage StudiesMachine TranslationClinical LanguageAutomatic GenerationVision Language ModelDeep LearningMedical Image ComputingMedical Language ProcessingComputer VisionMulti-modal SummarizationDiagnostic CaptioningLinguistics
Abstract Diagnostic captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination. DC can assist inexperienced physicians, reducing clinical errors. It can also help experienced physicians produce diagnostic reports faster. Following the advances of deep learning, especially in generic image captioning, DC has recently attracted more attention, leading to several systems and datasets. This article is an extensive overview of DC. It presents relevant datasets, evaluation measures, and up-to-date systems. It also highlights shortcomings that hinder DC’s progress and proposes future directions.
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