Viewing a single comment thread. View all comments

Adventurous-Mouse849 t1_je7gyqe wrote

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

And also data augmentation. Rotation, cropping, zooming. This is essential for data scarcity in medical imaging.

8

viertys OP t1_je9mlvj wrote

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?

1

Adventurous-Mouse849 t1_jedi4wq wrote

For augmentation that’s all bases covered. For more high-level or fully generative tasks I would also suggest mix-match (convex combo between similar samples). But you can’t justify that here bc you would have to relabel. Ultimately this does come down to too few images. If there’s a publicly available pretrained CT segmentation model you could fine-tune it to your task, or distill it’s weights to your model… just make sure they did a good job in the first place.

Also some other notes: I’d suggest sticking with distribution losses ie cross entropy. U-Net is sensitive to normalization so I’d also suggest training with and without normalized inputs.

1