Diagnostic captioning: a survey

被引：7
作者：
Pavlopoulos, John [1 ,2 ]
Kougia, Vasiliki [1 ,2 ]
Androutsopoulos, Ion [2 ]
Papamichail, Dimitris [3 ]
机构：
[1] Stockholm Univ, Dept Comp & Syst Sci, Borgarfjordsgatan 12, S-16455 Kista, Sweden
[2] Athens Univ Econ & Business, Dept Informat, Patission 76, Athens 10434, Greece
[3] Med Diagnost Ctr Kosmoiatriki, Nucl Med Dept, Patission 237, Athens 11254, Greece
来源：
KNOWLEDGE AND INFORMATION SYSTEMS | 2022年 / 64卷 / 07期
关键词：
Medical report generation; Natural language processing; Artificial intelligence; Diagnostic captioning; NATURAL-LANGUAGE GENERATION; ARTIFICIAL-INTELLIGENCE; RADIOLOGY; ACCURACY; FUTURE;
D O I：
10.1007/s10115-022-01684-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1691 / 1722
页数：32
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