MHIM-MIL [Updating]

Training codes and logs of Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification [ICCV 2023 Oral]

Prepare Patch Features

To preprocess WSIs, we used CLAM.

Patching

# for C16
python create_patches_fp.py --source DATA_DIRECTORY --save_dir RESULTS_DIRECTORY --patch_size 512 --step_size 512 --preset bwh_biopsy.csv --seg --patch
# for TCGA
python create_patches_fp.py --source DATA_DIRECTORY --save_dir RESULTS_DIRECTORY --patch_size 512 --step_size 512 --preset tcga.csv --seg --patch

Feature Extraction

CUDA_VISIBLE_DEVICES=0,1 python extract_features_fp.py --data_h5_dir DIR_TO_COORDS --data_slide_dir DATA_DIRECTORY --csv_path CSV_FILE_NAME --feat_dir FEATURES_DIRECTORY --batch_size 512 --target_patch_size=224 --slide_ext .svs

Training

Prepare Your Own Initiation Weight

# transmil baseline on C16 dataset
python3 main.py --project=$PROJECT_NAME --dataset_root=$DATASET_PATH --model_path=$OUTPUT_PATH --cv_fold=3 --title=transmil --model=pure --baseline=selfattn --seed=2021
# transmil baseline on TCGA dataset
python3 main.py --project=$PROJECT_NAME --datasets=tcga --dataset_root=$DATASET_PATH --model_path=$OUTPUT_PATH --cv_fold=4 --val_ratio=0.13 --title=transmil --model=pure --baseline=selfattn --seed=2021
# abmil baseline on C16 dataset
python3 main.py --project=$PROJECT_NAME --dataset_root=$DATASET_PATH --model_path=$OUTPUT_PATH --cv_fold=3 --title=abmil --model=pure --baseline=attn --seed=2021

Training MHIM-MIL

python3 main.py --project=$PROJECT_NAME --dataset_root=$DATASET_PATH --model_path=$OUTPUT_PATH --cv_fold=3 --teacher_init=./modules/init_ckp/c16_3fold_init_abmil_seed2021 --title=abmil_101_mr50h1-0r50_is --baseline=attn --num_workers=0 --cl_alpha=0.1 --mask_ratio_h=0.01 --mask_ratio_hr=0.5 --mrh_sche --init_stu_type=fc --mask_ratio=0.5 --mask_ratio_l=0. --seed=2021

python3 main.py --project=$PROJECT_NAME --dataset_root=$DATASET_PATH --model_path=$OUTPUT_PATH --cv_fold=3 --teacher_init=./modules/init_ckp/c16_3fold_init_transmil_seed2021 --mask_ratio_h=0.03 --mask_ratio_hr=0.5 --mrh_sche --title=transmil_101_sml80h3-0r50_mmcos_is --mask_ratio=0. --mask_ratio_l=0.8 --cl_alpha=0.1 --mm_sche --init_stu_type=fc --attn_layer=0 --seed=2021

Citing MHIM-MIL

@InProceedings{Tang_2023_ICCV,
    author    = {Tang, Wenhao and Huang, Sheng and Zhang, Xiaoxian and Zhou, Fengtao and Zhang, Yi and Liu, Bo},
    title     = {Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {4078-4087}
}