https://github.com/kapsner/ldm-ce-mip-lesions
https://github.com/kapsner/ldm-ce-mip-lesions
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/kapsner/ldm-ce-mip-lesions
- Owner: kapsner
- Created: 2023-03-17T11:46:04.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-11T08:08:17.000Z (about 2 years ago)
- Last Synced: 2025-03-01T22:32:48.561Z (over 1 year ago)
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ldm-ce-mips-lesions
This repository contains supporting material of the publication [Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model](https://www.nature.com/articles/s41598-024-56853-1).
Citation:
>Kapsner, L.A., Folle, L., Hadler, D. et al. Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model. Sci Rep 14, 6391 (2024). https://doi.org/10.1038/s41598-024-56853-1
## Installation
At first, prepare your data accordingly and adjust the [config file](configs/latent-diffusion/dce_mip-vq-seg.yaml) as well as the [data loader](https://github.com/kapsner/latent-diffusion/blob/main/ldm/data/dce_mip.py).
To run the code, then execute the following commands:
```bash
git clone -b v0.1.0 https://github.com/kapsner/latent-diffusion
cd latent-diffusion
conda env create -f environment.yaml
conda activate ldm
export parameter_grid=./param_grid_230119.csv
export ldm_cfg=./configs/latent-diffusion/dce_mip-vq-seg.yaml
export results_folder=/home/user/development/trainings/diffusionmodels
python automate_learnings.py \
-c $parameter_grid \
-b $ldm_cfg \
-l $results_folder
```