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config.py
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config.py
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import os
class Args(object):
def __init__(self, config_dict):
self.__dict__.update(config_dict)
class Config(object):
DEBUG=False
AGGREGATION="none"
ONCONET_CONFIG = {}
ONCODATA_CONFIG = {
'convertor': 'dcmtk',
'temp_img_dir': os.environ['TMP_IMG_DIR']
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCODATA_ARGS = Args(ONCODATA_CONFIG)
ONCOSERVE_VERSION = '0.2.0'
ONCODATA_VERSION = '0.2.0'
ONCONET_VERSION = '0.2.0'
ONCOQUERIES_VERSION = '0.2.0'
NAME = 'BaseConfig'
PORT = 5000
class DensityConfig(Config):
NAME = '2D_Mammo_Breast_Density'
AGGREGATION="vote"
def density_label_func(pred):
pred = pred.argmax()
density_labels = [1,2,3,4]
return density_labels[pred]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7662.53827604],
'img_std': [12604.0682836],
'img_size': [256,256],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'snapshot': 'snapshots/mgh_mammo_density_sep26_2018.pt',
'label_map': density_label_func,
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors':False,
'callibrator_path': None
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer1YrDetectionHybridConfig(Config):
NAME = '2D_Mammo_Cancer_1Year_Detection_Hybrid'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_1yr_detection_sep02_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': True,
'risk_factor_keys': "density binary_family_history binary_biopsy_benign binary_biopsy_LCIS binary_biopsy_atypical_hyperplasia age menarche_age menopause_age first_pregnancy_age prior_hist race parous menopausal_status weight height ovarian_cancer ovarian_cancer_age ashkenazi brca mom_bc_cancer_history m_aunt_bc_cancer_history p_aunt_bc_cancer_history m_grandmother_bc_cancer_history p_grantmother_bc_cancer_history sister_bc_cancer_history mom_oc_cancer_history m_aunt_oc_cancer_history p_aunt_oc_cancer_history m_grandmother_oc_cancer_history p_grantmother_oc_cancer_history sister_oc_cancer_history hrt_type hrt_duration hrt_years_ago_stopped",
'use_region_annotation': False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_1yr_detection_sep19_2018.pt'
}
ONCONET_CONFIG['risk_factor_keys'] = ONCONET_CONFIG['risk_factor_keys'].split()
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer5YrRiskHybridConfig(Config):
NAME = '2D_Mammo_Cancer_5Year_Risk_Hybrid'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_5yr_risk_hybrid_aug08_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': True,
'risk_factor_keys': "density binary_family_history binary_biopsy_benign binary_biopsy_LCIS binary_biopsy_atypical_hyperplasia age menarche_age menopause_age first_pregnancy_age prior_hist race parous menopausal_status weight height ovarian_cancer ovarian_cancer_age ashkenazi brca mom_bc_cancer_history m_aunt_bc_cancer_history p_aunt_bc_cancer_history m_grandmother_bc_cancer_history p_grantmother_bc_cancer_history sister_bc_cancer_history mom_oc_cancer_history m_aunt_oc_cancer_history p_aunt_oc_cancer_history m_grandmother_oc_cancer_history p_grantmother_oc_cancer_history sister_oc_cancer_history hrt_type hrt_duration hrt_years_ago_stopped",
"use_region_annotation": False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_5yr_risk_hybrid_aug08_2018.pt'
}
ONCONET_CONFIG['risk_factor_keys'] = ONCONET_CONFIG['risk_factor_keys'].split()
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer1YrDetectionConfig(Config):
NAME = '2D_Mammo_Cancer_1Year_Detection_Hybrid'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_1yr_detection_sep02_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': True,
'risk_factor_keys': "density binary_family_history binary_biopsy_benign binary_biopsy_LCIS binary_biopsy_atypical_hyperplasia age menarche_age menopause_age first_pregnancy_age prior_hist race parous menopausal_status weight height ovarian_cancer ovarian_cancer_age ashkenazi brca mom_bc_cancer_history m_aunt_bc_cancer_history p_aunt_bc_cancer_history m_grandmother_bc_cancer_history p_grantmother_bc_cancer_history sister_bc_cancer_history mom_oc_cancer_history m_aunt_oc_cancer_history p_aunt_oc_cancer_history m_grandmother_oc_cancer_history p_grantmother_oc_cancer_history sister_oc_cancer_history hrt_type hrt_duration hrt_years_ago_stopped",
'use_region_annotation': False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_1yr_detection_sep19_2018.pt'
}
ONCONET_CONFIG['risk_factor_keys'] = ONCONET_CONFIG['risk_factor_keys'].split()
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer5YrRiskImgOnlyConfig(Config):
NAME = '2D_Mammo_Cancer_5Year_Risk_ImgOnly'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_5yr_risk_img_only_aug08_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': False,
"use_region_annotation": False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_5yr_risk_img_only_aug08_2018.pt'
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer2YrRiskHybridConfig(Config):
NAME = '2D_Mammo_Cancer_2Year_Risk_Hybrid'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_2yr_risk_hybrid_aug10_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': True,
'risk_factor_keys': "density binary_family_history binary_biopsy_benign binary_biopsy_LCIS binary_biopsy_atypical_hyperplasia age menarche_age menopause_age first_pregnancy_age prior_hist race parous menopausal_status weight height ovarian_cancer ovarian_cancer_age ashkenazi brca mom_bc_cancer_history m_aunt_bc_cancer_history p_aunt_bc_cancer_history m_grandmother_bc_cancer_history p_grantmother_bc_cancer_history sister_bc_cancer_history mom_oc_cancer_history m_aunt_oc_cancer_history p_aunt_oc_cancer_history m_grandmother_oc_cancer_history p_grantmother_oc_cancer_history sister_oc_cancer_history hrt_type hrt_duration hrt_years_ago_stopped",
'use_region_annotation': False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_2yr_risk_hybrid_aug10_2018.pt'
}
ONCONET_CONFIG['risk_factor_keys'] = ONCONET_CONFIG['risk_factor_keys'].split()
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer2YrRiskImgOnlyConfig(Config):
NAME = '2D_Mammo_Cancer_2Year_Risk_ImgOnly'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_2yr_risk_img_only_aug07_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': False,
"use_region_annotation": False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_2yr_risk_img_only_aug07_2018.pt'
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancer1YrRiskImgOnlyConfig(Config):
NAME = '2D_Mammo_Cancer_1Year_Risk_ImgOnly'
AGGREGATION="max"
def cancer_risk_func(pred):
return pred[1]
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'label_map': cancer_risk_func,
'snapshot': 'snapshots/mgh_mammo_cancer_1yr_risk_nov22_2018.pt',
'video':False,
'use_precomputed_hiddens': False,
'use_risk_factors': False,
"use_region_annotation": False,
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrator_mgh_mammo_cancer_1yr_risk_nov22_2018.pt'
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCONET_ARGS = Args(ONCONET_CONFIG)
class MammoCancerMirai(Config):
NAME = '2D_Mammo_Cancer_Mirai'
AGGREGATION="max"
ONCONET_CONFIG = {
'cuda': False,
'img_mean': [7240.058],
'img_std': [12072.904],
'img_size': [1664,2048],
'num_chan': 3,
'num_gpus': 1,
'test_image_transformers': ['scale_2d', 'align_to_left'],
'test_tensor_transformers': ["force_num_chan_2d", "normalize_2d"],
'additional': None,
'snapshot': 'snapshots/mgh_mammo_cancer_survival_risk_5yr_mirai_full_jul02.p',
'video':False,
"pred_risk_factors": True,
'use_pred_risk_factors_at_test': True,
'pred_both_sides': True,
'multi_image': True,
'num_images': 4,
'min_num_images': 4,
'model_name': 'mirai_full',
'max_followup': 5,
'use_risk_factors': True,
"use_region_annotation": False,
'risk_factor_keys': "density binary_family_history binary_biopsy_benign binary_biopsy_LCIS binary_biopsy_atypical_hyperplasia age menarche_age menopause_age first_pregnancy_age prior_hist race parous menopausal_status weight height ovarian_cancer ovarian_cancer_age ashkenazi brca mom_bc_cancer_history m_aunt_bc_cancer_history p_aunt_bc_cancer_history m_grandmother_bc_cancer_history p_grantmother_bc_cancer_history sister_bc_cancer_history mom_oc_cancer_history m_aunt_oc_cancer_history p_aunt_oc_cancer_history m_grandmother_oc_cancer_history p_grantmother_oc_cancer_history sister_oc_cancer_history hrt_type hrt_duration hrt_years_ago_stopped",
'use_second_order_risk_factor_features': False,
'callibrator_path': 'snapshots/callibrators/MIRAI_FULL.callibrator.p'
}
ONCONET_ARGS = Args(ONCONET_CONFIG)
ONCONET_ARGS = Args(ONCONET_CONFIG)