Awesome

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation [MICCAI 2023]

[Paper]

0. Setup

Environment

the required packages are mostly same as openai/improved_diffusion Clone this repository and navigate to it in your terminal. Then run:

pip install -e .

Dataset

supported 2d datasets: Synapse(128x128), BraTS(224*224) The data folder structure is like:

.
├── ...
├── data                    
│   ├── brats_patch            
│       ├── flair
            ├── flair
                ├── training
                    ├── normal
                    ├── tumor 
                    ├── seg # label mask
                ├── validation

1. train DDPM models

for tumor images:

MODEL_FLAGS="--num_channels 128 --num_res_blocks 3  "
DIFFUSION_FLAGS="--diffusion_steps 4000 " # --use_kl True
TRAIN_FLAGS="--lr 1e-4 --batch_size 4" #--schedule_sampler loss-second-moment

python scripts/train_tumor_ddfm.py --save_dir runs/results/diff_brats \
--src_dir ${data_dir} --dataset brats \
$MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS

conditional training:

MODEL_FLAGS="--image_size 64 --num_channels 192 --num_res_blocks 3 --learn_sigma True --class_cond True"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 4"

python scripts/train_tumor_ddfm.py --save_dir runs/results/diff_brats \
--src_dir ${data_dir} --dataset brats \
$MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS

2. sampling from gaussian noise

The main file is scripts/image_tumor_sample.py, for different dataset, you need:

python scripts/image_tumor_sample.py

3. guided diffusion

Use an additional classifier model to guide the generation of samples, no need to retrain the DDPM model

Training models

Training a classifier

python scripts/classifier_tumor_train.py --save_dir runs/results/guided_diff_brats_cls_resnet \
--src_dir ${data_dir} --lr 3e-4 --batch_size 8 --model_type resnet50

Sampling:

python scripts/classifier_tumor_sample.py --save_dir runs/results/guided_diff_brats_cls_resnet \
--src_dir ${data_dir}

4. Weakly supervised segmentation

MODEL_FLAGS="--num_channels 128 --num_res_blocks 3 --learn_sigma True --class_cond True " # --learn_sigma True --class_cond True
DIFFUSION_FLAGS="--diffusion_steps 4000 "
TRAIN_FLAGS="--lr 3e-4 --batch_size 2"

python scripts/image_p_seg.py \
--save_dir {where_you_save_classifier} \
--model_path {where_you_save_diffusion} \
$MODEL_FLAGS $DIFFUSION_FLAGS -f 0 --batch_size 1 --guided