mlsanityData to 7 Oct 2026

Diagnostic Captioning: A Survey

arXiv 2101.07299 ↗First posted 18 Jan 2021cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 49 of its 120 references that are in the map. First posted on arXiv 18 Jan 2021.

Builds on

49 of 120 references.

References · 49

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37 more
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1.

6 papers.

  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2022Improving Chest X-Ray Report Generation by Leveraging Warm Startingcited by 93
  3. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  4. 2023A Systematic Review of Deep Learning-based Research on Radiology Report Generationcited by 14
  5. 2022Automotive Multilingual Fault Diagnosiscited by 0
  6. 2024UIT-DarkCow team at ImageCLEFmedical Caption 2024: Diagnostic Captioning for Radiology Images Efficiency with Transformer Modelscited by 0