Chang688/Dermodiff

基于边界感知的皮肤病灶分割扩散模型

软件23-1常炜 472321725

皮肤病变分割对皮肤病的早期发现和准确诊断起着至关重要的作用。消噪扩散概率模型(ddpm)最近因其出色的图像生成能力而受到关注。在这些进展的基础上，我们提出了DermoSegDiff，这是一个在学习过程中包含边界信息的皮肤病变分割的新框架。我们的方法引入了一种新的损失函数，在训练过程中对边界进行优先排序，逐渐降低其他区域的重要性。我们还介绍了一种新的基于u - net的去噪网络，该网络可以熟练地将网络内的噪声和语义信息集成在一起。在多个皮肤分割数据集上的实验结果表明，DermoSegDiff优于现有的基于CNN、transformer和diffusion的方法，显示了其在各种场景下的有效性和泛化性。

Network


Method

How to use

Requirements

• Ubuntu 16.04 or higher
• CUDA 11.1 or higher
• Python v3.7 or higher
• Pytorch v1.7 or higher
• Hardware Spec
  • GPU with 12GB memory or larger capacity (With low GPU memory you need to change and decrease dim_x_mults, dim_g_mults, dim_x, and dim_g params. You also need to change batch_size respectively. If you tune it well you won't lose considerable capability!)
  • For our experiments, we used 1GPU(A100-80G)

einops==0.6.1
ema_pytorch==0.2.3
matplotlib==3.7.2
numpy==1.24.4
opencv==4.6.0
opencv_python_headless==4.8.0.74
Pillow==10.0.0
PyYAML==6.0.1
scikit_image==0.19.3
scipy==1.6.3
termcolor==2.3.0
torch==2.0.1
torchvision==0.15.2
tqdm==4.65.0

pip install -r requirements.txt

Training

对于训练阶段，你需要选择相关的配置文件，并通过设置所需的目录和更改变量来修改它，如果需要的话，从' src '文件夹中运行以下命令，通过路径准备好的配置文件:

python src/training.py -c /path/to/config/file

你也可以在运行上面的命令时重载一些参数:

usage: [-h] -c CONFIG_FILE [-n MODEL_NAME] [-s INPUT_SIZE] [-b BATCH_SIZE] [-l LEARNING_RATE]
       [-t TIMESTEPS] [-S {linear,quadratic,cosine,sigmoid}] [-e EPOCHS] [--beta_start BETA_START]
       [--beta_end BETA_END] [-D [MODEL_DIM_MULTS [MODEL_DIM_MULTS ...]]] [-E ENSEMBLE]
       [--model_dim_x MODEL_DIM_X] [--model_dim_g MODEL_DIM_G]
       [--model_dim_x_mults [MODEL_DIM_X_MULTS [MODEL_DIM_X_MULTS ...]]]
       [--model_dim_g_mults [MODEL_DIM_G_MULTS [MODEL_DIM_G_MULTS ...]]]
       [--training_optimizer_betas [TRAINING_OPTIMIZER_BETAS [TRAINING_OPTIMIZER_BETAS ...]]]
       [--training_scheduler_factor TRAINING_SCHEDULER_FACTOR]
       [--training_scheduler_patience TRAINING_SCHEDULER_PATIENCE] [--augmentation_p AUGMENTATION_P]