The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Deep learning models in medical image analysis
Open accessAbstract
BACKGROUND Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice. HIGHLIGHT Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry. CONCLUSION The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.
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Deep learning in medical image analysis
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Medical Image Data Augmentation: Techniques, Comparisons and Interpretations
Goceri, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10453-z · Cited by 459
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
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Barreto, A. G., de Oliveira, J. M., Gois, F. N. B., Cortez, P. C., & de Albuquerque, V. H. C. (2023) · Bioengineering · doi:10.3390/bioengineering10091098 · Cited by 8
Metadata
- Authors
- Tsuneki, M.
- Venue
- Journal of Oral Biosciences
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 64
- Issue
- 3
- Pages
- 312-320
- ISSN
- 1349-0079 (ISSN-L)
- OpenAlex
- W4220868306
- Cited by
- 257
- References
- 76
- FWCI
- 11.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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