Submitted by viertys t3_125xdrq in MachineLearning
BreakingCiphers t1_je7dlg5 wrote
While testing models and playing with hyperparams can be fun, the real problem is that you are trying to apply deep learning to 100 images.
Get more images.
Adventurous-Mouse849 t1_je7gyqe wrote
And also data augmentation. Rotation, cropping, zooming. This is essential for data scarcity in medical imaging.
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?
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.
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