  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%), 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 medical image analysis. Also, we identified several
critical research gaps existing in the TL studies on medical image analysis.
