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Analysis of institutional authors

Albiol, AAuthor

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October 11, 2024
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Improving Radiology Report Generation Quality and Diversity through Reinforcement Learning and Text Augmentation

Publicated to:Bioengineering (Basel). 11 (4): 351- - 2024-04-01 11(4), DOI: 10.3390/bioengineering11040351

Authors: Parres, Daniel; Albiol, Alberto; Paredes, Roberto

Affiliations

Univ Politecn Valencia, Campus Vera,Cami Vera S-N - Author
Valencian Grad Sch & Res Network Artificial Intell, Cami Vera S-N - Author

Abstract

Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder-decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F1CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F1-scores of 66.2 for CheXbert and 37.8 for RadGraph on the MIMIC-CXR dataset, and 54.7 and 45.6, respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field.

Keywords

Chest x-raysDeep learningMachine learningMedical imageRadiology report generationReinforcement learningText augmentationText generationVision transformer

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Bioengineering (Basel) due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 51/124, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Biomedical. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Bioengineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-08-09:

  • WoS: 3
  • Scopus: 2
  • Europe PMC: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 14 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Parres, D) and Last Author (Paredes, R).

    the author responsible for correspondence tasks has been Parres, D.