KidsWithTokens / MedSegDiff

Medical Image Segmentation with Diffusion Model
MIT License
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The poor performances of the multi-labels BraTs dataset #138

Open smallboy-code opened 8 months ago

smallboy-code commented 8 months ago

The raw images:

T1 visdom_image (3) visdom_image (2) visdom_image (1)

The Ground Truth: 图片1

The output sample of 5 ensembles:

图片3

So why these outputs contain the brain boundary? And how to set 0,1, 2, 4?

princerice commented 8 months ago

I would like to know your batch and the number of training steps

smallboy-code commented 8 months ago

batch_size: 32 and training steps : 30000

princerice commented 8 months ago

Thank you for your answer. My batch is set to 8 and I use A5000GPU. I would like to know the size of the GPU memory you use

smallboy-code commented 8 months ago

I using the four A100 GPUs. And I get the normal mask without brain tissue boundary, but the Dice is lower, about 0.4. So do you have this issue?

princerice commented 8 months ago

I trained 40,000 steps on a gpu and the results are not good and we may need to adjust and improve ourselves

princerice commented 8 months ago

I'm also confused that the loss has leveled off but the results are far from satisfactory

smallboy-code commented 8 months ago

Yes, and I want to know the clamp range in the final "process_xstart" function. I am use (0,3).

princerice commented 8 months ago

This part is not clear to me yet, I am just trying to reproduce and learn the code