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A Systematic Review of Deep Learning-based Research on Radiology Report Generation

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arxiv 2311.14199 v2 pith:FLYA22VR submitted 2023-11-23 cs.CV cs.CL

Chang Liu , Yuanhe Tian , Yan Song This is my paper · ORCID
classification cs.CVcs.CL
keywords approachesdeepgenerationreportresearchclinicalcross-modaldifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Radiology report generation (RRG) aims to automatically generate free-text descriptions from clinical radiographs, e.g., chest X-Ray images. RRG plays an essential role in promoting clinical automation and presents significant help to provide practical assistance for inexperienced doctors and alleviate radiologists' workloads. Therefore, consider these meaningful potentials, research on RRG is experiencing explosive growth in the past half-decade, especially with the rapid development of deep learning approaches. Existing studies perform RRG from the perspective of enhancing different modalities, provide insights on optimizing the report generation process with elaborated features from both visual and textual information, and further facilitate RRG with the cross-modal interactions among them. In this paper, we present a comprehensive review of deep learning-based RRG from various perspectives. Specifically, we firstly cover pivotal RRG approaches based on the task-specific features of radiographs, reports, and the cross-modal relations between them, and then illustrate the benchmark datasets conventionally used for this task with evaluation metrics, subsequently analyze the performance of different approaches and finally offer our summary on the challenges and the trends in future directions. Overall, the goal of this paper is to serve as a tool for understanding existing literature and inspiring potential valuable research in the field of RRG.

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Cited by 3 Pith papers

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  1. PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

    cs.CV 2026-07 conditional novelty 6.0 of 10

    PanDent links tooth-level dental annotations to template-aligned reports and shows that current MLLMs fail fine-grained dental diagnosis while fine-tuning on it improves localization and accuracy.

  2. HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    HC-LLM adds time-shared and time-specific feature constraints to large language models for longitudinal radiology report generation, reporting state-of-the-art BLEU and clinical efficacy scores on Longitudinal-MIMIC.

  3. A Survey of Medical Vision-and-Language Applications and Their Techniques

    cs.CV 2024-11 conditional novelty 4.0 of 10

    This survey reviews medical vision-and-language models across five tasks and organizes existing methods, datasets, and evaluation metrics without introducing new techniques.

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