3D Convolution

2015년 도입 · 논문 208편에서 사용

A 3D Convolution is a type of convolution where the kernel slides in 3 dimensions as opposed to 2 dimensions with 2D convolutions. One example use case is medical imaging where a model is constructed using 3D image slices. Additionally video based data has an additional temporal dimension over images making it suitable for this module. Image: Lung nodule detection based on 3D convolutional neural networks, Fan et al