Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Open accessAbstract
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
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Initializing the network with a pretrained model trained on relatively large datasets can significantly accelerate training convergence, reduce overfitting, and improve accuracy to some extent.
“initializing the network with a pretrained model that has been trained with relatively large datasets can significantly accelerate the training convergence, reduce overfitting, and improve the accuracy to some extent (Tajbakhsh et al., 2016).”
From Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease (2021) · cites this paper as Tajbakhsh et al., 2016
The Fine-Tuning section (p. 3) describes initializing from a network pretrained on a massive labeled dataset. The Discussion (p. 11) reports faster convergence and comparable or better performance with adequate fine-tuning. The Introduction (p. 1) identifies overfitting as a problem when training from scratch, but the paper does not establish that pretrained initialization reduces overfitting. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHandling unseen objects is a major advantage over traditional CNNs and even transfer learning or weakly supervised learning, because for some objects it requires no annotated training instances.
“Dealing with unseen objects is a major advantage compared with traditional CNNs and even transfer learning [25] or weakly supervised learning [3], since for some objects it does not require annotated instances for training at all.”
From Interactive medical image segmentation using deep learning with image-specific fine tuning (2018) · cites this paper as [25]
The challenge of generalization extends to variations in imaging protocols, patient populations, and staining techniques in digital pathology.
“This challenge extends to variations in imaging protocols, patient populations, and even staining techniques in digital pathology [125,126].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [126]
Transfer learning from natural images has been widely adopted for medical image classification; however the application to medical image segmentation has been scarce.
“Transfer learning from natural images has been widely adopted for medical image classification Tajbakhsh et al. (2016); Shin et al. (2016); however the application to medical image segmentation has been scarce.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Tajbakhsh et al. (2016)
Fine-tuning a pre-trained model generally performs better than a model trained from scratch.
“These findings were reinforced in [120] to demonstrate that fine-tuning a pre-trained model generally performs better than a model trained from scratch.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [120]
The requirement of large labeled training samples is difficult to meet in current medical US analysis, where expert annotation is expensive and some diseases are scarce in datasets.
“However, this requirement is difficult to meet in current medical US analysis, where expert annotation is expensive and where some diseases (e.g., lesions or nodules) are scarce in the datasets [68].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [68]
The convolutional encoder-decoder network using VGG16 shares many features with the commonly used U-Net network but has unique advantages, including lower computation cost, batch normalization layers to improve training efficiency, and more versatility for use as both a segmentation and classification network, allowing effective transfer learning.
“The convolutional encoder-decoder network using VGG16 shares many features with the commonly used U-Net network (29) but has unique advantages, including lower computation cost, presence of batch normalization layers to improve training efficiency, and more versatility for use as both a segmentation and classification network, which allows effective transfer learning in the current setup (30,31).”
From Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. (2018) · cites this paper as [31]
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- Authors
- Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J.
- Affiliations
- Arizona State University (US)
- Mayo Clinic (US)
- Mayo Clinic in Arizona (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1299-1312
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2346062110
- Cited by
- 3,277
- References
- 81
- Citations by year
- 2026: 307 · 2025: 345 · 2024: 322 · 2023: 338 · 2022: 366 · 2021: 427 · 2020: 387 · 2019: 385 · 2018: 256 · 2017: 117 · 2016: 25 · 2012: 1
- FWCI
- 214.97
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- convolutional neural networks, transfer learning, fine-tuning, pre-trained models, training from scratch, layer-wise fine-tuning, medical image analysis, radiology, gastroenterology, image classification, object detection, image segmentation, training data size
- MeSH terms
- Machine Learning, Computed Tomography Angiography, Colonic Polyps, Colonoscopy, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Pulmonary Embolism, ROC Curve, Neural Networks, Computer
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