Pre-trained 3D ResNet-101

バージョン 1.0.1 (192 MB) 作成者: Amir Ebrahimi

Pre-trained Neural Network Toolbox Model for 3D ResNet-101 Network

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更新 2021/5/6

ライセンスの表示

To transfer the learnable parameters from pre-trained 2D ResNet-101 (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-101 learns patterns in each frame. This model has 87 million learnable parameters.

simply, call "resnet101TL3Dfunction()" function.

引用

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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作成: R2020b

R2019b 以降のリリースと互換性あり

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ヒントを得たファイル: Deep Learning Toolbox Model for ResNet-101 Network, Deep Learning Network Analyzer for Neural Network Toolbox

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resnet101TL3Dfunction.mlx

バージョン	公開済み	リリースノート
1.0.1	2021/5/6	The relevant paper is published.	ダウンロード
1.0.0	2021/2/16		ダウンロード

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} }