mlsanityData to 7 Oct 2026

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

arXiv 2004.13175 ↗First posted 27 Apr 2020eess.IV cs.CV cs.LGcited by 54

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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.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2020.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 13; 2024: 14; 2025: 7; 2026: 1.

54 papers.

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  1. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  2. 2022What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factorscited by 61
  3. 2023A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Taskscited by 36
  4. 2021Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labelscited by 32
  5. 2022Self-Supervised Pretraining for 2D Medical Image Segmentationcited by 24
  6. 2021Challenges for machine learning in clinical translation of big data imaging studiescited by 15
  7. 2023Pretrained ViTs Yield Versatile Representations For Medical Imagescited by 13
  8. 2024Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classificationcited by 13
  9. 2022On the Generalization for Transfer Learning: An Information-Theoretic Analysiscited by 11
  10. 2022Probabilistic Integration of Object Level Annotations in Chest X-ray Classificationcited by 10
  11. 2023I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnosescited by 10
  12. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
38 more
  1. 2025Vision Foundation Models in Medical Image Analysis: Advances and Challengescited by 9
  2. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  3. 2025WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Miningcited by 8
  4. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  5. 2022Self-Supervised Endoscopic Image Key-Points Matchingcited by 4
  6. 2023Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machinescited by 3
  7. 2024FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysiscited by 3
  8. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  9. 2024Enhancing weed detection performance by means of GenAI-based image augmentationcited by 3
  10. 2024Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networkscited by 2
  11. 2024Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impactcited by 2
  12. 2021Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learningcited by 1
  13. 2021Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoringcited by 1
  14. 2021Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationcited by 1
  15. 2021Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rayscited by 1
  16. 2021Multi-loss ensemble deep learning for chest X-ray classificationcited by 1
  17. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  18. 2023Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countriescited by 1
  19. 2024Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathologycited by 1
  20. 2026Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Networkcited by 1
  21. 2021Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chestcited by 0
  22. 2022Quantitative Imaging Principles Improves Medical Image Learningcited by 0
  23. 2022The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanningcited by 0
  24. 2022Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognitioncited by 0
  25. 2023A Survey on Deep Industrial Transfer Learning in Fault Prognosticscited by 0
  26. 2023Multi-task neural networks by learned contextual inputscited by 0
  27. 2023Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2cited by 0
  28. 2023One-step learning algorithm selection for classification via convolutional neural networkscited by 0
  29. 2023Osteosarcoma Tumor Detection using Transfer Learning Modelscited by 0
  30. 2023Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023cited by 0
  31. 2023Review of AlexNet for Medical Image Classificationcited by 0
  32. 2024BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learningcited by 0
  33. 2024Towards Efficient Active Learning in NLP via Pretrained Representationscited by 0
  34. 2024Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvementcited by 0
  35. 2024Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imagingcited by 0
  36. 2024GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentationcited by 0
  37. 2024Enhancing Skin Lesion Classification Generalization with Active Domain Adaptationcited by 0
  38. 2025Overfitting in Histopathology Model Training: The Need for Customized Architecturescited by 0

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