viertys

viertys OP t1_je9srha wrote

Reply to comment by deep-yearning in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys

All images have cavities in them and in general the cavities make up 5-10% of the image.

Here is an example: https://imgur.com/a/z0yeH0C The mask on the left is the ground truth and the mask on the right is the predicted one.

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I'm currently using Kaggle and I can't use very large batch sizes. My batch size is 4 now. Is there an alternative to Kaggle that you would suggest?

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viertys OP t1_je9nno8 wrote

Reply to comment by BrotherAmazing in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys

I didn't mention it in the post, but I'm using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?
I have an accuracy of 98.50 and I have dice of around 0.30-0.65 in each image

And yes, the images are grayscale and they are cropped around the teeth area, so only that part of the X-ray remains.

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viertys OP t1_je9m4ao wrote

Reply to comment by Yeinstein20 in Improvements/alternatives to U-net for medical images segmentation? by viertys

I didn't mention it in the post, but I'm using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?

I have an accuracy of 98.50 and I have dice of around 0.30-0.65 in each image

1