Pre-trained 3D ResNet-18

Version 1.0.3 (44.7 MB) by Amir Ebrahimi

Pre-trained Neural Network Toolbox Model for 3D ResNet-18 Network

1.8K Downloads

Updated 6 May 2021

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.

Cite As

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.

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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.

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MATLAB Release Compatibility

Created with R2020b

Compatible with R2019b and later releases

Platform Compatibility

Windows macOS Linux

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Acknowledgements

Inspired by: Deep Learning Toolbox Model for ResNet-18 Network, Deep Learning Network Analyzer for Neural Network Toolbox

Inspired: 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

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resnet18TL3Dfunction.mlx

Version	Published	Release Notes
1.0.3	6 May 2021	The relevant paper is published.	Toolbox Zip
1.0.2	26 Feb 2021	The related paper is updated.	Toolbox Zip
1.0.1	1 Jan 2021	The relevant paper is published.	Zip Toolbox
1.0.0	10 Nov 2020		Zip Toolbox

MLA	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.
APA	Ebrahimi, A., Luo, S., & Chiong, R. (2020). Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images. In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. Retrieved from https://doi.org/10.1109%2Fivcnz51579.2020.9290616
BibTeX	@inproceedings{Ebrahimi_2020, doi = {10.1109/ivcnz51579.2020.9290616}, url = {https://doi.org/10.1109%2Fivcnz51579.2020.9290616}, year = 2020, month = {nov}, publisher = {{IEEE}}, author = {Amir Ebrahimi and Suhuai Luo and Raymond Chiong}, title = {Introducing Transfer Learning to 3D {ResNet}-18 for Alzheimer's Disease Detection on {MRI} Images}, booktitle = {2020 35th International Conference on Image and Vision Computing New Zealand ({IVCNZ})} }

MLA	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.
APA	Ebrahimi, A., Luo, S., & Alzheimer’s Disease Neuroimaging Initiative, for the. (2021). Convolutional neural networks for Alzheimer’s disease detection on MRI images. Journal of Medical Imaging, 8(02). SPIE-Intl Soc Optical Eng. Retrieved from https://doi.org/10.1117%2F1.jmi.8.2.024503
BibTeX	@article{Ebrahimi_2021, doi = {10.1117/1.jmi.8.2.024503}, url = {https://doi.org/10.1117%2F1.jmi.8.2.024503}, year = 2021, month = {apr}, publisher = {{SPIE}-Intl Soc Optical Eng}, volume = {8}, number = {02}, author = {Amir Ebrahimi and Suhuai Luo and for the Alzheimer's Disease Neuroimaging Initiative}, title = {Convolutional neural networks for Alzheimer's disease detection on {MRI} images}, journal = {Journal of Medical Imaging} }