def fix_metadata(metadata):
flag_edited = False
for midx, patient_prediction in enumerate(metadata['predictions']):
pid = patient_prediction['patient']
pset, pindex = data_loader.id_to_index_map[pid]
pfolder = data_loader.patient_folders[pset][pindex]
pnrslices = data_loader.compute_nr_slices(pfolder)
if pnrslices < 6 and not _is_empty_prediction(patient_prediction):
print(" Removing patient %d" % pid)
_clean_prediction(patient_prediction)
flag_edited = True
assert _is_empty_prediction(patient_prediction)
return flag_edited
def fix_metadata(metadata):
flag_edited = False
for midx, patient_prediction in enumerate(metadata['predictions']):
pid = patient_prediction['patient']
pset, pindex = data_loader.id_to_index_map[pid]
pfolder = data_loader.patient_folders[pset][pindex]
pnrslices = data_loader.compute_nr_slices(pfolder)
if pnrslices < 6 and not _is_empty_prediction(patient_prediction):
print " Removing patient %d" % pid
_clean_prediction(patient_prediction)
flag_edited = True
assert _is_empty_prediction(patient_prediction)
return flag_edited
def filter_samples(folders):
# don't use patients who don't have more than 6 slices
return [
folder for folder in folders
if data_loader.compute_nr_slices(folder) > 6
]
def filter_samples(folders):
# don't use patients who don't have more than 6 slices
return [
folder for folder in folders
if data_loader.compute_nr_slices(folder) > 6]
