A scoping review of transfer learning research on medical image analysis using ImageNet

by   Mohammad Amin Morid, et al.

Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome. Materials and Methods: To identify relevant studies, MEDLINE, IEEE, and ACM digital library were searched. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study protocol defined a priori. Results: After screening of 8,421 articles, 102 met the inclusion criteria. Of 22 anatomical areas, eye (18 brain (12 72 Inception models were the most commonly used in breast related studies (50 while VGGNet was the common in eye (44 AlexNet for brain (42 frequently used models. Inception models were the most frequently used for studies that analyzed ultrasound (55 X-rays (57 tomography images (50 (36 other well-trained CNN models and 33 interpretation. Discussion: Various methods have been used in TL approaches from non-medical to medical image analysis. The findings of the scoping review can be used in future TL studies to guide the selection of appropriate research approaches, as well as identify research gaps and opportunities for innovation.


page 12

page 13

page 14


Medical Image Analysis using Convolutional Neural Networks: A Review

Medical image analysis is the science of analyzing or solving medical pr...

Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions

Motivation: Medical image analysis involves tasks to assist physicians i...

Dataset Growth in Medical Image Analysis Research

Medical image analysis studies usually require medical image datasets fo...

A Tour of Unsupervised Deep Learning for Medical Image Analysis

Interpretation of medical images for diagnosis and treatment of complex ...

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification

Transfer learning is a commonly used strategy for medical image classifi...

Few-shot learning for medical text: A systematic review

Objective: Few-shot learning (FSL) methods require small numbers of labe...

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Deep-learning (DL) algorithms are becoming the standard for processing u...

Please sign up or login with your details

Forgot password? Click here to reset