Questions tagged [3d-convolution]
14 questions
10
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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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2
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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
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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1
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…
ado sar
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0
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What is the best type of input for a 3D UNet?
I want to use 3D U-Net (or similar) network to create a 3D reconstruction of my microscopy data.
The original paper for the 3D U-Net (https://arxiv.org/abs/1606.06650) describes the implementation with orthogonal slices (xy, yz, zx planes) of the…
sam
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How many pretraining image is enough for Swin Transformer?
Here is the spec of experiment setup:
We have 3D micro CT image of the rats, and we want to perform pretraining on such data. The image is masked, so only the portion around the backbone is visible.
Question for experts in the medical imaging field,…
0
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0 answers
What is the best way for learning an output matrix from different size of input vectors and matrix?
What is the most effective method for learning the NXM matrix (for each sample) in relation to different size input vectors and matrices?
To make it more clear:
I have a 56x32 output matrix for my first sample, and the corresponding input set is…
Ayska
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0
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1 answer
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 answers
ML model giving rank errors on 3D layers on converting 2D images to 3D models
i am currently working on a model to convert 2d images to 3d models through a ml model. For this i have taken into reference a research paper which had this diagrammatical flow of layers & i have tried to put that into work but in vain. Would really…
quest1001
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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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How to convert prediction probabilities of 2D images (initially 3D image) to 3D image predictions?
Classification: binary
Model: CNN (ResNet50V2)
During our research we've had 91x109x91 images (3-dimensional). We've used 2D CNN to train and evaluate our images and make predictions on labelled cases, thus we had to convert 3D to 2D this way…
Amadej Šenk
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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…
0
votes
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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