Convolutional Neural Networks for Medical Image Analysis Fine Tuning or Full Training
Abstract
Convolutional Neural Networks for Medical Image Analysis Fine Tuning or Full Training.
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,
Conclusion
In this Convolutional Neural Networks for Medical Image Analysis Fine Tuning or Full Training paper, we aimed to address the following central question in the context of medical image analysis:
Our results are important because they show that knowledge transfer from natural images to medical images is possible, even though the relatively large difference between source and target databases is suggestive that such application may not be possible.
