Pre-trained 3D ResNet-18
Pre-trained Neural Network Toolbox Model for 3D ResNet-18 Network
To transfer the learnable parameters from pre-trained 2D ResNet-18 (ImageNet) to 3D one, we duplicated 2D filters (copying them repeatedly) through the third dimension. This is possible since a video or a 3D image can be converted into a sequence of image slices. In the training process, we expect that the 3D ResNet-18 learns patterns in each frame. This model has 34 million learnable parameters.
simply, call "resnet18TL3Dfunction()" function.
引用格式
Ebrahimi, Amir, et al. “Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images.” 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE, 2020, doi:10.1109/ivcnz51579.2020.9290616.
Ebrahimi, Amir, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021, doi:10.1117/1.jmi.8.2.024503.
致谢
参考作品: Deep Learning Toolbox Model for ResNet-18 Network, Deep Learning Network Analyzer for Neural Network Toolbox
启发作品: Alzheimer’s Disease Detection using 3D ResNet-18 on MRI, Alzheimer’s Disease Detection using multi-modal 3D data, Pre-trained 3D ResNet-50, Alzheimer’s Disease Detection using multi-modal 3D data
| 版本 | 已发布 | 发行说明 | Action |
|---|---|---|---|
| 1.0.3 | The relevant paper is published. |
|
|
| 1.0.2 | The related paper is updated. |
|
|
| 1.0.1 | The relevant paper is published. |
|
|
| 1.0.0 |
|
