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U-Net and U-Net inspired architectures have been quite popular in the medical image-related tasks ever since it was first introduced. There have been several improved versions of U-Net designed for specific tasks that followed. One such example is Attention U-Net, extremely popular for Pancreas Segmentation. Other examples of architectures that have ...

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The point is that in the expansive path you have two forms of information: the information from the contracting path, which includes all high-level features extracted from the original image. the information from the skip-connections, which copy a cropped version of the feature maps in the contracting path. Because, as we go forward through the expansive ...

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It turns out that the solution to this problem is in the version of h5py. If you have h5py == 3.1.* then you need to downgraded it to h5py == 2.10.0 Version of Keras needs to be 2.2.4 Or you can upgrade the version of Keras to 2.2.5

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CNN is used since it is effectively an optimized use case for dealing with image data. CNN effectively automatically extracts features from images. Other techniques are more likely to not take full advantage of the data. CNN is able to make full use of the data by also including information from adjacent pixels and downsample through layers. Here is a ...

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In this example you have a gray scale image of size 572x572 and 1 (gray) channel. The first convolution operation consists of 64 filters of size 3x3 and 1 channel per filter. The channel of the filters always fits the channel size of the previous layer (here: the Input). In the second convolution step of this explicit architecture, you again use 64 filters ...

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What you want to do is called multi-task learning. Here's what you do: Create a second Input. Attach it to 1D CNN (2-3 layers), so it aggregates this tabular information. Concatenate this feature with the intermediate feature generated by the U-Net using Concatenate layer. Put a dense layer of 2 after this. Put softmax with units = number of classes. Add CE ...

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You can try doing image segmentation the traditional way, just using the image data. If you want to use the non-image data, then, you can introduce classification as another task for your network. It will provide some regularization to your model. But, this is one way you can still use non-image data whilst still working with image outputs.

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Well, some time ago I also faced the same issue in the semantic segmentation task. Batch normalization is expected to improve convergence, because the normalization of activations prevents the explosion of the gradients magnitude and leads to more steady convergence. Adam is an adaptive optimizer with momentum and division by the weighted sum of gradients on ...

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Yes, $E$ is the cross-entropy function and a direct generalization of the binary case. For the binary case, probability to belong to the class $1$ is given by a sigmoid function $\sigma(x)$ of the output $x$, and the probability to belong to the class $0$ is $1 - \sigma(x)$. Therefore the binary crossentropy will give:  -\sum_i (l_i \log \sigma(x) + (1 - ...

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Similar to other answers, I don't know Matlab that well but you could try the following steps to debug your problem. Make sure you can overfit to a single instance from your dataset, pull out a single image with a good amount of true positives in it. Duplicate that images B times (where B = Batch Size) and then try to train your network with only that small ...

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You can find leaderboards as well as code at this address. For now, HRNetV2 leads the game. The U-Net architecture is part of a broad family of network architectures that aggregate multi-scale features to extract finer details useful for semantic segmentation. Examples are Feature Pyramidal Networks (FPN), Hourglass, Encoder-Decoder, MatrixNet, etc...

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