Sample code & model (to be updated in a few months) for MICCAI2023 Efficient Subclass Learning
paper: https://arxiv.org/abs/2307.00257 (MICCAI 2023 early accepted!)
run run.sh and set the corresponding parameters to train a model and test.py to evaluate its validity (you might want to take a look at how arguments are defined in train_proposed.py and test.py). Currently configured datasets are ACDC and BraTS2021, be sure to include the correct dataset in the data_path argument in train_**.py, it is needed for the parser to make the right choice for the dataloader.
dataset structure for ACDC:
ACDC
|----data
| |----slices (for all slices used for training)
| | |----patient_000_frame01_slice_0.h5
| | |----patient_000_frame01_slice_1.h5
| | |----...
| |----patient_000_frame01.h5 (you should first package each image and its labels in a h5 file)
| |----patient_000_frame02.h5
| |----...
|----train.list (for all train slices, note you should include only the names for .h5 files)
|----val.list (for all val instances)
|----test.list (for all test instances)
|----mapping.json (for multi-foreground cases)
and for other datasets:
<dataset_name>
|----data
| |----***.h5 (you should first package each image and its labels in a h5 file)
| |----...
|----train.list
|----val.list
|----test.list
|----mapping.json
sample train.list contents:
patient_000_frame01\n
patient_000_frame02\n
...
sample mapping.json contents:
{1: [1, 2, 3], 2: [4, 5], 3: [6]} (note these ascending sequence orders must be satisfied)
combine image and its fine label to h5:
image, fine_label -> h5['image'], h5['label']; h5['granularity']=1
for image that does not have fine labels:
image, coarse_label -> h5['image'], h5['label']; h5['granularity']=0