Questions tagged [3d-convolution]

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3D Unet gives "output size is too small" error [closed]

I wrote simple 3D-Unet arch in pytorch to do segmentation on 3D images. ...
user1631306's user avatar
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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 ...
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Feeding variable length of 2D image slices of the MRI into the deep neural network

I am trying to build a classifier that would predict the correct outcome (disease vs healthy) using a set of 2D slices derived from the 3D MRI scan. For each patient, based on the 3D scan, I am able ...
Oleg's user avatar
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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 ...
sam's user avatar
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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 ...
ado sar's user avatar
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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. ...
Sherry Yuan's user avatar
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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. ...
david's user avatar
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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 ...
quest1001's user avatar
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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's user avatar
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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,...
Amadej Šenk's user avatar
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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 ...
OverLordGoldDragon's user avatar
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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 ...
satan 29's user avatar
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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 ...
hanugm's user avatar
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6 votes
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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 ...
Charlie Parker's user avatar
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2 answers
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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, ...
Shobhit Verma's user avatar
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1 answer
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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 ...
Kicr's user avatar
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