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A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks
Open accessAbstract
The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used.
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“Synthetic data generation using generative models can create realistic, diverse data sets that mimic real-world multimodal medical scenarios, thus expanding the training data set without compromising privacy or ethical standards [149-151].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [151]
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References 17 · Cited by 8
Research works printed but not linked: 1
- Pelka, O., Koitka, S., Rückert, J., Nensa, F., and Friedrich, C.M. (2018). Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, Springer International Publishing.
Metadata
- Authors
- Barreto, A. G., de Oliveira, J. M., Gois, F. N. B., Cortez, P. C., & de Albuquerque, V. H. C.
- Affiliations
- Universidade Federal do Ceará (BR)
- Graduate Program in Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil
- Controladoria e Ouvidoria Geral do Estado, Governo do Estado do Ceará, Fortaleza 60822-325, Brazil
- Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil
- ORCID
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2023-09-19
- Volume
- 10
- Issue
- 9
- Pages
- 1098
- ISSN
- 2306-5354 (ISSN-L)
- PMC
- PMC10525548
- OpenAlex
- W4386893702
- Cited by
- 8
- References
- 17
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioengineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- CNPq (313599/2019-0, 305517/2022-8)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- generative models, transformer models, convolutional neural networks, medical image feature extraction, medical report generation
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- The paper
- Barreto, A. G., de Oliveira, J. M., Gois, F. N. B., Cortez, P. C., & de Albuquerque, V. H. C. (2023). A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks. Bioengineering. https://doi.org/10.3390/bioengineering10091098
