MedficientSAM: A Robust Medical Segmentation Model with Optimized Inference Pipeline for Limited Clinical Settings
git clone --recursive https://github.com/hieplpvip/medficientsam.git
cd medficientsam
conda env create -f environment.yaml -n medficientsam
conda activate medficientsam| System | Ubuntu 22.04.5 LTS |
|---|---|
| CPU | AMD EPYC 7742 64-Core Processor |
| RAM | 256GB |
| GPU (number and type) | One NVIDIA A100 40G |
| CUDA version | 12.0 |
| Programming language | Python 3.10 |
| Deep learning framework | torch 2.2.2, torchvision 0.17.2 |
Accuracy metrics are evaluated on the public validation set of CVPR 2024 Segment Anything In Medical Images On Laptop Challenge. The computational metrics are obtained on an Intel(R) Core(TM) i9-10900K.
| Model | Resolution | Params | FLOPs | DSC | NSD | 2D Run Time | 3D Run Time | 2D Memory Usage | 2D Memory Usage |
|---|---|---|---|---|---|---|---|---|---|
| MedficientSAM-L0 | 512x512 | 34.79M | 36.80G | 85.85% | 87.05% | 0.9s | 7.4s | 448MB | 687MB |
| MedficientSAM-L1 | 512x512 | 47.65M | 51.05G | 86.42% | 87.95% | 1.0s | 9.0s | 553MB | 793MB |
| MedficientSAM-L2 | 512x512 | 61.33M | 70.71G | 86.08% | 87.53% | 1.1s | 11.1s | 663MB | 903MB |
The Docker images can be found here.
docker load -i seno.tar.gz
docker container run -m 8G --name seno --rm -v $PWD/test_input/:/workspace/inputs/ -v $PWD/test_output/:/workspace/outputs/ seno:latest /bin/bash -c "sh predict.sh"
To measure the running time (including Docker starting time), see https://github.com/bowang-lab/MedSAM/blob/LiteMedSAM/CVPR24_time_eval.py
You need to participate in the challenge to access the dataset.
After downloading it, copy .env.example to .env and modify CVPR2024_MEDSAM_DATA_DIR to correct path.
The directory structure should look like this:
CVPR24-MedSAMLaptopData
├── train_npz
│ ├── CT
│ ├── Dermoscopy
│ ├── Endoscopy
│ ├── Fundus
│ ├── Mammography
│ ├── Microscopy
│ ├── MR
│ ├── OCT
│ ├── PET
│ ├── US
│ └── XRay
├── validation-box
└── imgs
See train_scripts.
You can download the weights here.
To run MedficientSAM-L1 on the validation set:
python src/infer.py experiment=infer_finetuned_l1
See configs/experiment/infer_* for running other model variants.
python src/export_onnx.py experiment=export_finetuned_l0_cpp output_dir=weights/finetuned-l1-augmented/e2_cpp
docker build -f Dockerfile.cpp -t seno.fat .
slim build --target seno.fat --tag seno --http-probe=false --include-workdir --mount $PWD/test_input/:/workspace/inputs/ --mount $PWD/test_output/:/workspace/outputs/ --exec "sh predict.sh"
docker save seno | gzip -c > seno.tar.gz