For questions related to the U-net, a neural network proposed in "U-Net: Convolutional Networks for Biomedical Image Segmentation" (2015) by Olaf Ronneberger et al. for semantic segmentation.
Questions tagged [u-net]
46 questions
8
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1 answer
Validation accuracy higher than training accurarcy
I implemented the unet in TensorFlow for the segmentation of MRI images of the thigh. I noticed I always get a higher validation accuracy by a small gap, independently of the initial split. One example:
So I researched when this could be…
Lis Louise
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4
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2 answers
Unet Overfitting for binary segmentation of fake images
I am working on a project where I am trying to detect and localize forgeries in images. I am using the CASIA v2 dataset and using Unet model for the task. I have the binary masks of all the images in the CASIA v2 dataset. The metric I am using for…
Yishu Malhotra
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4
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1 answer
What are some good alternatives to U-Net for biomedical image segmentation?
Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best).
Is there a neural network, that can compete with U-Net, in this case?
I've spent the last few hours searching…
Nuwanda
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4
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2 answers
What are the best algorithms for image segmentation tasks?
I recently started looking for networks that focus on image segmentation tasks related to biomedical applications. I could not miss the publication U-Net: Convolutional Networks for Biomedical Image Segmentation (2015) by Ronneberger, Fischer, and…
Denis Joly
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4
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0 answers
What are some neural network models that can use auxiliary info during training for image segmentation?
What are some deep learning models that can use supplementary information other than RGB channels for image segmentation?
For example, imagine a poorly shot image of a river (blue) that shows a gap, and the supplementary information is detailed…
Nanako Honda
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3
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1 answer
Why diffusion model always use U-Net?
I want to know why diffusion models always use U-Net.
In my opinion, they use U-Net because you can see features of different resolutions and skip connection is good to add detail of images. But I am not sure if it is the main reason why they use…
Penguin.jpg
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3
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1 answer
Learning an identity function with convolutional networks
I am trying to train networks to achieve what I expected to be a trivial task: learn the identity mapping. However, this is very hard to achieve, and the optimization is hard.
Moreover, I don't want to learn $f_\theta(x)=x\;\;\forall x$, but only…
Franco Marchesoni
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3
votes
1 answer
What is the use of the regular convolutional layer in expansion path of U-Net?
I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional layers in the contracting path, but I can't figure…
Bhuwan Bhatt
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3
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3 answers
If I trained a model to perform semantic segmentation on images with only one object, would it also work on images with multiple objects?
I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting out, say, ligaments, from a medical image. If I train that model on images that…
cmed123
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2
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How do we stack two U-Nets to yield one final prediction?
I am trying to reproduce the model described in the paper DocUNet: Document Image Unwarping via A Stacked U-Net, i.e. stacking two U-Nets to yield one final prediction. The paper mentions that:
The deconvolution features of the first U-Net and the…
aRRay
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2
votes
1 answer
What is the role of skip connections in U-Net?
I was able to find that the skip connections used in U-Net help to recover fine grained details in the prediction, however I do not understand what is meant by this. Besides, I was wondering what would happen if the U-Net does not include skip…
TRM
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2
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1 answer
What does the "number of channels" correspond to in U-Net?
I'm studying the U-Net CNN architecture. I'm new to CNNs and am confused regarding the "number of channels".
Referring to the U-Net diagram, the input image is convolved with a 3x3 mask which generates a 570x570 output. This output image is then…
Izzo
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2
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0 answers
How and why do state-of-the-art models in medical segmentation differ from general segmentation models?
I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet.
What I haven't understood yet: Why are these approaches better for medical…
Bert Gayus
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2
votes
0 answers
Why do I get higher average dice accuracy for less data
I am working on image segmentation of MRI thigh images with deep learning (Unet). I noticed that I get a higher average dice accuracy over my predicted masks if I have less samples in the test data set.
I am calculating it in tensorflow as
def…
Lis Louise
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2
votes
0 answers
Getting bounding box/boundaries from segmentations in UNet Nuclei Segmentation
From my understanding, in a tissue where nuclei are present and need to be detected, we need to predict bounding boxes (either rectangular/circular or in the shape of the nucleus, i.e. as in instance segmentation). However, a lot of research papers…
Prasanjit Rath
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