The model is available in Mathwork's file exchange. It was initially used for Alzheimer's disease detection.
To transfer the learnable parameters from pre-trained 2D CNN (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 CNN learns patterns in each frame. This model has 34 million learnable parameters.
Available models: ResNet-18, ResNet-50, ResNet-101, LeNet-5
simply, call "resnet18TL3Dfunction()" function or any other function.
Ebrahimi, Amir, et al. “Introducing Transfer Leaming 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.
