基于深度扩散模型的脑部MRI中的病灶定位方法内含数据集和环境搭建教程.zip

# Diff-SCM ## Overview Diff-SCM aims at developing solutions based diffusion probabilistic modeling (DPM) for causal machine learning. Our [algorithm](https://arxiv.org/abs/2202.10166) perform counterfactual inference using Pearl's abduction-action-prediction strategy using a deterministic inference based on a deterministic diffusion inference ([DDIM](https://arxiv.org/abs/2010.02502)). We have also [applied](https://arxiv.org/abs/2207.12268) to the method for lesion localization in Brain MRI. The code is this repo implements the following papers: > Sanchez, Pedro, and Sotirios A. Tsaftaris. "Diffusion Causal Models for Counterfactual Estimation." In *Conference on Causal Learning and Reasoning* (CLeaR). 2022. > Sanchez, Pedro, Kascenas, Antanas, Liu, Xiao, O'Neil, Alison and Sotirios A. Tsaftaris. "What is Healthy? Generative Counterfactual Diffusion for Lesion Localization." In *Deep Generative Models workshop at MICCAI 2022* (DGM4Miccai). 2022. Note. If one is looking for particular bits of code with our contributions, consider checking the `diff_scm/sampling/sampling_utils.py` file. The code for counterfactual estimation, classfier-free (implicit) inference and dynamic normalisation is there. ## How to run ### Install The provided `.yml` file can be use for creating a conda environment with the following command. `conda env create -f environment.yml` ### Hyperparameters A few hyperparameters are particularly important for counterfactual estimation. In particular, the parameters the control the strenght of the intervention. The `sampling.classifier_scale`, `sampling.norm_cond_scale`, `sampling.sampling_progression_ratio` are the some of these and can be found in the `diff_scm/configs` folder. Also, se this folder for example configuration on MNIST and BRATS dataset.