Summary of Comparison Of Fine-tuning Strategies For Transfer Learning in Medical Image Classification, by Ana Davila et al.
Comparison of fine-tuning strategies for transfer learning in medical image classification
by Ana Davila, Jacinto Colan, Yasuhisa Hasegawa
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates various fine-tuning methods applied to pre-trained models in medical imaging, evaluating eight strategies across five domains: X-ray, MRI, Histology, Dermoscopy, and Endoscopic surgery. The authors select three well-established CNN architectures (ResNet-50, DenseNet-121, and VGG-19) to cover a range of learning scenarios. Although the results show varying efficacy depending on the architecture and medical imaging type, strategies like combining Linear Probing with Full Fine-tuning demonstrate general effectiveness across domains. Additionally, Auto-RGN, which adjusts learning rates, leads to performance enhancements of up to 11% for specific modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to make pre-trained models work better in medical imaging. It tries different approaches on five types of images: X-ray, MRI, histology, dermatoscopy, and endoscopic surgery. The researchers use three popular computer vision models (ResNet-50, DenseNet-121, and VGG-19) to see how they perform. They find that some methods work better than others, but some approaches can improve results by up to 11%. This helps us understand how to make pre-trained models more useful in medical imaging. |
Keywords
» Artificial intelligence » Cnn » Fine tuning » Resnet
