Pre-trained 3D ResNet-101

버전 1.0.1 (192 MB) 작성자: Amir Ebrahimi

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

팔로우

0.0

(0)

다운로드 수: 321

업데이트 날짜: 2021/5/6

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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.

인용 양식

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 릴리스 호환 정보

개발 환경: R2020b

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도움

도움 받은 파일: Deep Learning Toolbox Model for ResNet-101 Network, Deep Learning Network Analyzer for Neural Network Toolbox

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resnet101TL3Dfunction.mlx

버전	게시됨	릴리스 정보
1.0.1	2021/5/6	The relevant paper is published.	다운로드
1.0.0	2021/2/16		다운로드

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} }