Suyog Jadhav

I haven't yet tried the code on the BraTS 2018 dataset, mainly due to computational bottleneck. The author works at Nvidia and had access to a DGX-1. He has described...

I haven't written the data augmentation code yet. The author did standard normalization and nothing much anyways. Citing from the paper directly, **3.7 Data preprocessing and augmentation** _We normalize all...

You can try adjusting the weights of the loss terms (KL and L2). These seem to be playing pivotal role. Also, you may need to train for around 200-300 epochs...

Can you try removing the minus sign from this line and see if it improves the result? https://github.com/IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization/blob/7471e81923741aa1b3593e4e95fc090c2e96c8cc/model.py#L143

Also, is there a reason why the MRI scans in the second figure are blurry? Are they representing the output of VAE branch?

That might be a problem, I created the notebook after I had verified the model to be working on Brats18 dataset, so it might be the case that I accounted...

Hello @CraigMyles, This script was written before TensorFlow2.0 was released. I am not sure how things work now, but since the model itself is designed in Keras, you should be...

To get predictions on a test image, you can use ```python model.predict(img) ``` Where ```img``` is a numpy array. You can save with ```cv2.imwrite```.

This works actually. At least it used to in TensorFlow 1.x. I forgot to account for the custom loss function. My bad. Anyway, this is how you do it. You...

Hmm, weird. Haven't seen that error before. Will try to look into it. In the meantime, if you manage to solve it, please do share the solution here.