Slide-SAM: Medical SAM meets sliding window

We upload the SlideSAM-H checkpoint recently! Please download by

Slide-SAM-B: https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA [code：7be9]

SlideSAM-H: https://pan.baidu.com/s/1jnOwyWd-M1fBIauNi3IA4w [code: 05dy]

Before Training

install tutils

pip install trans-utils

prepare datasets

We recommend you to convert the dataset into the nnUNet format.

00_custom_dataset
  imagesTr
    xxx_0000.nii.gz
    ...
  labelsTr
    xxx.nii.gz
    ...

try to use the function organize_in_nnunet_style or organize_by_names to prepare your custom datasets.

Then run

python -m  datasets.generate_txt

A [example]_train.txt will be generated in ./datasets/dataset_list/

The content should be like below

01_BCV-Abdomen/Training/img/img0001.nii.gz	01_BCV-Abdomen/Training/label/label0001.nii.gz
01_BCV-Abdomen/Training/img/img0002.nii.gz	01_BCV-Abdomen/Training/label/label0002.nii.gz
01_BCV-Abdomen/Training/img/img0003.nii.gz	01_BCV-Abdomen/Training/label/label0003.nii.gz

cache 3d data into slices

After generating the [example]_train.txt file, check the config file configs/vit_b.yaml.

Update the params in dataset by yours. And the dataset_list should be the name of the generated txt file [example].

Then run

python -m datasets.cache_dataset3d

Training

run training

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m core.ddp --tag debug

Testing

python -m core.volume_predictor

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