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

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Morid, Mohammad Amin, Borjali, Alireza, Del Fiol, Guilherme·2021·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2020.104115

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Abstract

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 for studies published between June 1st, 2012 and January 2nd, 2020. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study protocol defined a priori. Results After screening of 8421 articles, 102 met the inclusion criteria. Of 22 anatomical areas, eye (18%), breast (14%), and brain (12%) were the most commonly studied. Data augmentation was performed in 72% of fine-tuning TL studies versus 15% of the feature-extracting TL studies. Inception models were the most commonly used in breast related studies (50%), while VGGNet was the common in eye (44%), skin (50%) and tooth (57%) studies. AlexNet for brain (42%) and DenseNet for lung studies (38%) were the most frequently used models. Inception models were the most frequently used for studies that analyzed ultrasound (55%), endoscopy (57%), and skeletal system X-rays (57%). VGGNet was the most common for fundus (42%) and optical coherence tomography images (50%). AlexNet was the most frequent model for brain MRIs (36%) and breast X-Rays (50%). 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation. Discussion This study identified the most prevalent tracks of implementation in the literature for data preparation, methodology selection and output evaluation for various medical image analysis tasks. Also, we identified several critical research gaps existing in the TL studies on medical image analysis. The findings of this 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.

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Research works printed but not linked: 1

Cheplygina, 2019, Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, Med. Image Anal., 54, 280, 10.1016/j.media.2019.03.009 · DOI · Paper page

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Authors: Morid, M. A., Borjali, A., & Del Fiol, G.
Affiliations: Santa Clara University (US)
Harvard University (US)
Corresponding author: Morid, Mohammad Amin
ORCID: Morid, Mohammad Amin
Borjali, Alireza
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2021-01
Volume: 128
Pages: 104115
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2020.104115
OpenAlex: W3020996329
Cited by: 508
References: 152
Citations by year: 2026: 68 · 2025: 92 · 2024: 113 · 2023: 107 · 2022: 81 · 2021: 42
FWCI: 4.43
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Computers in Biology and Medicine (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: transfer learning, medical image analysis, ImageNet, convolutional neural networks, fine-tuning, feature extraction, data augmentation, Inception model, VGG, AlexNet, DenseNet, ocular imaging, breast imaging, brain MRI, ultrasound, endoscopy, optical coherence tomography, fundus images, research gaps
MeSH terms: Machine Learning, Mammography, Tooth, Neural Networks, Computer

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

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