Questions tagged [3d-convolution]
12 questions
11
votes
2 answers
When should I use 3D convolutions?
I am new to convolutional neural networks, and I am learning 3D convolution. What I could understand is that 2D convolution gives us relationships between low-level features in the X-Y dimension, while the 3D convolution helps detect low-level…
Shobhit Verma
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6
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1 answer
Is there any use of using 3D convolutions for traditional images (like cifar10, imagenet)?
I am curious if there is any advantage of using 3D convolutions on images like CIFAR-10/100 or ImageNet. I know that they are not usually used on this data set, though they could because the channel could be used as the "depth" channel.
I know that…
Charlie Parker
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3
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1 answer
Which neural network architectures are there that perform 3D convolutions?
I am trying to do 3d image deconvolution using convolution neural network. But I cannot find many famous CNNs that perform a 3d convolution. Can anyone point out some for me?
Background: I am using PyTorch, but any language is OK. What I want to…
Kicr
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2
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How do I use a 2D image segmentation model on 3D medical imaging data?
I am trying to use a high-level semantic segmentation model (something like DeepLabv3), that takes in 2D RGB images, and then fine-tune it for my problem. However, I am working with brain MRI images which are grayscale 3D images.
The obvious…
Tarek Alkabbani
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2
votes
2 answers
Improving validation losses and accuracy for 3D CNN
I have used a 3D CNN architecture, for detecting the presence of a particular promoter (MGMT), by using FLAIR brain scans. (64 slices per patient). The output is supposed to be binary (0/1).
I have gone through the pre-processing properly, and used…
satan 29
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vote
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Do all CNNs learn to detect edges in the first layer?
I was looking at 3D CNNs that process volumetric data, e.g. for MRI images of brain, where the input is a 4D tensor, and I couldn't find images from the filters of the first layer.
Suppose that detecting a spherical shape is very important for the…
Antonios Sarikas
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Which generative model architecture (and loss function) should I use to train on a dataset of 3D arrays of embedded tokens?
I've curated a dataset of player-made Minecraft builds. Each unique Minecraft block is tokenized and treated as a unique "word" like in NLP. I've trained a Skip-Gram model on the dataset (using context "cubes" and target "blocks" as opposed to…
schmixi
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0
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1 answer
3D Unet gives "output size is too small" error
I wrote simple 3D-Unet arch in pytorch to do segmentation on 3D images.
class UNet3D(nn.Module):
def __init__(self, in_channels, out_channels):
super(UNet3D, self).__init__()
# Encoder
self.encoder = nn.Sequential(
…
user1631306
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0
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Is it overfitting?
hi i'm new in this field. I am trying to do a video classification project by using 3DCNN and I plotted the loss curves & accuracy curves. I have some questions.
i'm using kfold Cross validation. Should i save the parameters after every fold and…
david
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0
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1 answer
Reconstructing 3D models from 2D images using autoencoders
I went through a research paper ("Voxel-Based 3D Object Reconstruction from Single 2D Image Using Variational Autoencoders") and tried to implement the approach following this diagram:
arizona_3
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2D models on 3D tasks (convolutions): simple replace?
2D tasks enjoy a vast backing of successful models that can be reused.
For convolutions, can one simply replace 2D operations with 3D counterparts and inherit their benefits? Any 'extra steps' to improve the transition? Not interested in unrolling…
OverLordGoldDragon
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0
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2 answers
What do people refer to when they use the word 'dimensionality' in the context of convolutional layer?
In practical applications, we generally talk about three types of convolution layers: 1-dimensional convolution, 2-dimensional convolution, and 3-dimensional convolution. Most popular packages like PyTorch, Keras, etc., provide Conv1d, Conv2d, and…
hanugm
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