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

arXiv: https://arxiv.org/abs/2004.13175 · HTML: https://mlsanity.com/paper/2004.13175/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 30 of 54)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors](https://mlsanity.com/paper/2203.01825/) (2022)
- [A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks](https://mlsanity.com/paper/2310.04270/) (2023)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Self-Supervised Pretraining for 2D Medical Image Segmentation](https://mlsanity.com/paper/2209.00314/) (2022)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification](https://mlsanity.com/paper/2402.07595/) (2024)
- [Pretrained ViTs Yield Versatile Representations For Medical Images](https://mlsanity.com/paper/2303.07034/) (2023)
- [On the Generalization for Transfer Learning: An Information-Theoretic Analysis](https://mlsanity.com/paper/2207.05377/) (2022)
- [Probabilistic Integration of Object Level Annotations in Chest X-ray Classification](https://mlsanity.com/paper/2210.06980/) (2022)
- [I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses](https://mlsanity.com/paper/2309.13550/) (2023)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Vision Foundation Models in Medical Image Analysis: Advances and Challenges](https://mlsanity.com/paper/2502.14584/) (2025)
- [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022)
- [WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining](https://mlsanity.com/paper/2503.04106/) (2025)
- [MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis](https://mlsanity.com/paper/2308.16561/) (2023)
- [Self-Supervised Endoscopic Image Key-Points Matching](https://mlsanity.com/paper/2208.11424/) (2022)
- [Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines](https://mlsanity.com/paper/2311.15966/) (2023)
- [FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis](https://mlsanity.com/paper/2403.13341/) (2024)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Enhancing weed detection performance by means of GenAI-based image augmentation](https://mlsanity.com/paper/2411.18513/) (2024)
- [Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks](https://mlsanity.com/paper/2405.03235/) (2024)
- [Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact](https://mlsanity.com/paper/2412.19124/) (2024)
- [Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning](https://mlsanity.com/paper/2103.02982/) (2021)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification](https://mlsanity.com/paper/2107.05940/) (2021)
- [Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays](https://mlsanity.com/paper/2108.04329/) (2021)
- [Multi-loss ensemble deep learning for chest X-ray classification](https://mlsanity.com/paper/2109.14433/) (2021)
- [CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images](https://mlsanity.com/paper/2110.10813/) (2021)
- [Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries](https://mlsanity.com/paper/2308.02597/) (2023)

## Abstract (arXiv)

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.