Pre-trained 3D ResNet-50
To transfer the learnable parameters from pre-trained 2D ResNet-50 (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-50 learns patterns in each frame. This model has 48 million learnable parameters.
simply, call "resnet50TL3Dfun()" function.
Zitieren als
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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Inspiriert von: Deep Learning Toolbox Model for ResNet-50 Network, Deep Learning Network Analyzer for Neural Network Toolbox, Pre-trained 3D ResNet-18
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Version | Veröffentlicht | Versionshinweise | |
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1.0.1 | The relevant paper is published. |
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