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Medical Image Segmentation with Diffusion Model

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The raw images: ![T1](https://github.com/WuJunde/MedSegDiff/assets/61675340/8b6ed862-1a3b-4130-9733-05c1b9f207d9) ![visdom_image (3)](https://github.com/WuJunde/MedSegDiff/assets/61675340/011ada15-2fb6-4270-8737-5786c90422ca) ![visdom_image (2)](https://github.com/WuJunde/MedSegDiff/assets/61675340/01125e40-87dc-4fdb-b4d7-2a9753b4a501) ![visdom_image (1)](https://github.com/WuJunde/MedSegDiff/assets/61675340/279d2abf-7ded-4f40-8629-920e69c13c65) The Ground Truth: ![图片1](https://github.com/WuJunde/MedSegDiff/assets/61675340/c9fab943-1617-43d5-a0e2-b6d327f385e9) The output sample of 5 ensembles: ![图片3](https://github.com/WuJunde/MedSegDiff/assets/61675340/078d3584-2af3-4ac2-87ea-fe2bd27ca869) So why these outputs contain the brain boundary? And...

Thank you for your excellent work! Is there a pretrained model of diffusion model that can be provided at training time.

I trained the model on the ISIC dataset with the default experimental settings provided in the readme and sampled the same image using the weight files of 15,000, 65,000 and...

if dice_score(final["sample"][:,-1,:,:].unsqueeze(1), final["cal"]) < 0.65: cal_out = torch.clamp(final["cal"] + 0.25 * final["sample"][:,-1,:,:].unsqueeze(1), else: cal_out = torch.clamp(final["cal"] * 0.5 + 0.5 * final["sample"][:,-1,:,:].unsqueeze(1), 0, 1) What is the main purpose of...

Hi there, nice work. Can you provide me your training and testing split for the BRATS21 dataset? I am trying to reproduce your work so I would like to know...

After setting use_fp16 = True, some bugs occur: ![image](https://github.com/WuJunde/MedSegDiff/assets/61099841/73354af9-48d3-4238-b5ac-26ecb7b3d957) It seems that not all of the models and weights are converted to fp16? Looking forward to your reply, thank you

作者你好,我在训练ISIC数据集的时候,训练了2w7个step,使用ddim sample可以得到较好的结果,但是当我使用dpm-solver之后效果会变得很差,请问这是什么原因呢? dpm-solver的配置是50个step。 期待您的回复

Hi Great job! I have some question when i used "DPM-solver=True". 1: Does the batchsize must be 1 when used DPM-solver ? if not in dpm_solver.py def dynamic_thresholding_fn(self, x0, t):...

![image](https://github.com/WuJunde/MedSegDiff/assets/71435435/8f9f030b-76cf-41a0-a650-103b3670e392) Thank you for your great job. I am relly interesting to your job V1 and V2. Could you please explain the mean of larger variance generated by MedSegDiff-V1. Why...