Transfer learning techniques for medical image analysis: A review
Document Type
Article
Publication Date
1-1-2022
Abstract
Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Auto-mated medical image analysis techniques can overcome these limitations. In this review, we investigated Transfer Learning (TL) architectures for automated medical image analysis. We discovered that TL has been applied to a wide range of medical imaging tasks, such as segmentation, object identification, disease categorization, severity grading, to name a few. We could establish that TL provides high quality decision support and requires less training data when compared to traditional deep learning methods. These advantageous properties arise from the fact that TL models have already been trained on large generic datasets and a task specific dataset is only used to customize the model. This eliminates the need to train the models from scratch. Our review shows that AlexNet, ResNet, VGGNet, and Goo-gleNet are the most widely used TL models for medical image analysis. We found that these
Keywords
Medical image, Machine learning, Convolutional neural networks, Transfer learning
Divisions
biomed
Funders
None
Publication Title
Biocybernetics and Biomedical Engineering
Volume
42
Issue
1
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS