# Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification arXiv: https://arxiv.org/abs/2402.07595 ยท HTML: https://mlsanity.com/paper/2402.07595/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 67 references that are in the map. - First posted on arXiv 12 Feb 2024. ## Builds on (most cited first, 23 of 23) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021) - [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023) - [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019) - [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019) - [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016) - [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020) - [Are Natural Domain Foundation Models Useful for Medical Image Classification?](https://mlsanity.com/paper/2310.19522/) (2023) - [Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks](https://mlsanity.com/paper/2312.02366/) (2023) - [Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects](https://mlsanity.com/paper/2312.04578/) (2023) - [Reproducibility in machine learning for medical imaging](https://mlsanity.com/paper/2209.05097/) (2022) - [Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation](https://mlsanity.com/paper/2311.11176/) (2023) ## Built on by (most cited first, 14 of 14) - [Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2](https://mlsanity.com/paper/2411.15802/) (2024) - [MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis](https://mlsanity.com/paper/2509.06617/) (2025) - [Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations](https://mlsanity.com/paper/2504.08584/) (2025) - [Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging](https://mlsanity.com/paper/2506.18434/) (2025) - [GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2511.15256/) (2025) - [ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection](https://mlsanity.com/paper/2606.01300/) (2026) - [Revisiting Vehicle Color Recognition in Long-Tailed Surveillance Scenarios](https://mlsanity.com/paper/2606.13625/) (2026) - [DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretability](https://mlsanity.com/paper/2407.18100/) (2024) - [DINO-LG: Enhancing Vision Transformers with Label Guidance for Coronary Artery Calcium Detection](https://mlsanity.com/paper/2411.07976/) (2024) - [Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2](https://mlsanity.com/paper/2511.05509/) (2025) - [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025) - [Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT Images](https://mlsanity.com/paper/2505.15120/) (2025) - [Exploring the Impact of Skin Color on Skin Lesion Segmentation](https://mlsanity.com/paper/2603.29694/) (2026) - [Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models](https://mlsanity.com/paper/2610.18543/) (2026) ## Abstract (arXiv) Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of foundational models like the DINOv2, which uses the vision transformer architecture, has opened new opportunities in the field and gathered significant interest. However, DINOv2's performance on clinical data still needs to be verified. In this paper, we performed a glioma grading task using three clinical modalities of brain MRI data. We compared the performance of various pre-trained deep learning models, including those based on ImageNet and DINOv2, in a transfer learning context. Our focus was on understanding the impact of the freezing mechanism on performance. We also validated our findings on three other types of public datasets: chest radiography, fundus radiography, and dermoscopy. Our findings indicate that in our clinical dataset, DINOv2's performance was not as strong as ImageNet-based pre-trained models, whereas in public datasets, DINOv2 generally outperformed other models, especially when using the frozen mechanism. Similar performance was observed with various sizes of DINOv2 models across different tasks. In summary, DINOv2 is viable for medical image classification tasks, particularly with data resembling natural images. However, its effectiveness may vary with data that significantly differs from natural images such as MRI. In addition, employing smaller versions of the model can be adequate for medical task, offering resource-saving benefits. Our codes are available at https://github.com/GuanghuiFU/medical_DINOv2_eval.