MedicineToken / 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 12 months ago

smallboy-code commented 12 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 11 months ago

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

smallboy-code commented 11 months ago

batch_size: 32 and training steps : 30000

princerice commented 11 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 11 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 11 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 11 months ago

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

smallboy-code commented 11 months ago

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

princerice commented 11 months ago

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