Deep learning models in medical image analysis

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. (c) 2022 Japanese Association for Oral Biology. Published by Elsevier B.V. All rights reserved.

Automated summary

Deep learning is a powerful technology for medical image analysis that offers fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures. However, the lack of sufficient medical images for training sets poses a major limitation for deep learning in medical image analysis.