DenseNet121_Chexpert_WBCE_E20_B32 - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained DenseNet121 architecture using the 'Chexpert_WBCE_E20_B32' benchmark. The benchmark was initialized for the chexpert_full dataset with batch size of 32, shuffel set to True and images rescaled to dimension (256, 256). The training was done for 20 epochs using the Adam optimizer and binary_crossentropy loss with class weights. A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 143410 number of sample, the validation set 35635, and the test set 44369.
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import os
import re
import pprintdata = json.loads(os.environ['EXP_DATA'])
history = data['history']
for s in data["description"].split(".")[:-1]:
print(s + ".\n")Trained DenseNet121 architecture using the 'Chexpert_WBCE_E20_B32' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 32, shuffel set to True and images rescaled to dimension (256, 256).
The training was done for 20 epochs using the Adam optimizer and binary_crossentropy loss with class weights.
A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 143410 number of sample, the validation set 35635, and the test set 44369.
# if there are any metrics that were renamed, add this new name here as ("default_name":"new_name")
metric_custom_names={"auc":"AUC_ROC"}
metric_names = [re.sub("([a-z0-9])([A-Z])","\g<1> \g<2>",name) for name in data["benchmark"]["metrics"]]
metric_keys = [re.sub("([a-z0-9])([A-Z])","\g<1>_\g<2>",name).lower() for name in data["benchmark"]["metrics"]]
for default_name, custom_name in metric_custom_names.items():
if not default_name in history.keys() and default_name in metric_keys:
#replace default name with custom name
metric_keys[metric_keys.index(default_name)]=custom_namedef print_or_plot_metric(metric_key, metric_name, figure_name):
if len(history[metric_key]) == 1:
print("Data for {m_name} only available for a single epoch. \nSkipping plot and printing data...".format(m_name=metric_name))
print('Train {}: '.format(metric_name), history[metric_key])
print('Validation {}: '.format(metric_name), history['val_'+metric_key])
print()
else:
plot_epoch_metric(metric_key, metric_name, figure_name)
def plot_epoch_metric(metric_key, metric_name, figure_name):
figure(num=None, figsize=(10, 6))
plt.plot(history[metric_key])
if 'val_'+metric_key in history.keys():
plt.plot(history['val_'+metric_key])
plt.title(figure_name)
plt.ylabel(metric_name)
plt.xlabel('Epoch')
if 'val_'+metric_key in history.keys():
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
for i, metric_key in enumerate(metric_keys):
print_or_plot_metric(metric_key, metric_names[i], "Model "+metric_names[i])
print_or_plot_metric("loss", "Loss", "Model loss")
if "lr" in history.keys():
plot_epoch_metric("lr", "Learning Rate", "Learning Rate")
if 'classification_report' in data.keys() and data['classification_report']:
print(data['classification_report']) precision recall f1-score support
No Finding 0.11 0.10 0.10 4574
Enlarged Cardiomediastinum 0.06 0.04 0.05 2022
Cardiomegaly 0.13 0.14 0.13 5522
Lung Opacity 0.46 0.18 0.26 20674
Lung Lesion 0.04 0.01 0.02 1709
Edema 0.24 0.18 0.21 10348
Consolidation 0.06 0.01 0.01 2922
Pneumonia 0.03 0.00 0.00 1215
Atelectasis 0.13 0.01 0.02 6714
Pneumothorax 0.08 0.04 0.05 3652
Pleural Effusion 0.39 0.37 0.38 17290
Pleural Other 0.02 0.00 0.01 694
Fracture 0.04 0.01 0.01 1726
Support Devices 0.51 0.52 0.52 23077
micro avg 0.36 0.25 0.29 102139
macro avg 0.16 0.12 0.13 102139
weighted avg 0.33 0.25 0.27 102139
samples avg 0.28 0.23 0.23 102139
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3142490088939667
Test auc: 0.7549559473991394
Test precision: 0.6636328101158142
Test recall: 0.46258530020713806
Test f2_score: 0.49242109060287476
Test binary_accuracy: 0.8729780912399292
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_WBCE_E20_B32',
'dataset_folder': 'data/chexpert/full',
'dataset_name': 'chexpert_full',
'dim': [256, 256],
'drop_last': True,
'epochs': 20,
'label_columns': [ 'No Finding',
'Enlarged Cardiomediastinum',
'Cardiomegaly',
'Lung Opacity',
'Lung Lesion',
'Edema',
'Consolidation',
'Pneumonia',
'Atelectasis',
'Pneumothorax',
'Pleural Effusion',
'Pleural Other',
'Fracture',
'Support Devices'],
'loss': 'binary_crossentropy',
'metrics': ['auc', 'precision', 'recall', 'f2_score', 'binary_accuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'negative_weights': [ 1.110120177268982,
1.051700472831726,
1.1367506980895996,
1.899986743927002,
1.0429342985153198,
1.30768883228302,
1.0704970359802246,
1.0280543565750122,
1.1742662191390991,
1.096386432647705,
1.6260361671447754,
1.015804648399353,
1.0428282022476196,
2.0840952396392822],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 10.080989837646484,
20.34217643737793,
8.312580108642578,
2.1111273765563965,
24.291378021240234,
4.25003719329834,
15.185005187988281,
36.6450309753418,
6.738346099853516,
11.374900817871094,
2.5973517894744873,
64.27252197265625,
24.349124908447266,
1.9224282503128052],
'shuffle': True,
'test_num_samples': 44369,
'train_num_samples': 143410,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': True,
'valid_num_samples': 35635}
Version: 1
Trained DenseNet121 architecture using the 'Chexpert_WBCE_E20_B32' benchmark. The benchmark was initialized for the chexpert_full dataset with batch size of 32, shuffel set to True and images rescaled to dimension (256, 256). The training was done for 20 epochs using the Adam optimizer and binary_crossentropy loss with class weights. A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 143410 number of sample, the validation set 35635, and the test set 44369.
from pathlib import Path
from dotenv import load_dotenv, find_dotenv
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import os
import re
import pprint
basepath = Path(os.getcwd())
if basepath.name != "idp-radio-1":
os.chdir(basepath.parent.parent)
print(os.getcwd())
load_dotenv(find_dotenv())
from src.preprocessing.split.train_test_split import train_test_split/srv/idp-radio-1
data = json.loads(os.environ['EXP_DATA'])
history = data['history']for s in data["description"].split(".")[:-1]:
print(s + ".\n")Trained DenseNet121 architecture using the 'Chexpert_WBCE_E20_B32' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 32, shuffel set to True and images rescaled to dimension (256, 256).
The training was done for 20 epochs using the Adam optimizer and binary_crossentropy loss with class weights.
A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 143410 number of sample, the validation set 35635, and the test set 44369.
# if there are any metrics that were renamed, add this new name here as ("default_name":"new_name")
metric_custom_names={"auc":"AUC_ROC"}
metric_names = [re.sub("([a-z0-9])([A-Z])","\g<1> \g<2>",name) for name in data["benchmark"]["metrics"]]
metric_keys = [re.sub("([a-z0-9])([A-Z])","\g<1>_\g<2>",name).lower() for name in data["benchmark"]["metrics"]]
for default_name, custom_name in metric_custom_names.items():
if not default_name in history.keys() and default_name in metric_keys:
#replace default name with custom name
metric_keys[metric_keys.index(default_name)]=custom_namedef print_or_plot_metric(metric_key, metric_name, figure_name):
if len(history[metric_key]) == 1:
print("Data for {m_name} only available for a single epoch. \nSkipping plot and printing data...".format(m_name=metric_name))
print('Train {}: '.format(metric_name), history[metric_key])
print('Validation {}: '.format(metric_name), history['val_'+metric_key])
print()
else:
plot_epoch_metric(metric_key, metric_name, figure_name)
def plot_epoch_metric(metric_key, metric_name, figure_name):
figure(num=None, figsize=(10, 6))
plt.plot(history[metric_key])
if 'val_'+metric_key in history.keys():
plt.plot(history['val_'+metric_key])
plt.title(figure_name)
plt.ylabel(metric_name)
plt.xlabel('Epoch')
if 'val_'+metric_key in history.keys():
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
for i, metric_key in enumerate(metric_keys):
print_or_plot_metric(metric_key, metric_names[i], "Model "+metric_names[i])
print_or_plot_metric("loss", "Loss", "Model loss")
if "lr" in history.keys():
plot_epoch_metric("lr", "Learning Rate", "Learning Rate")
if 'classification_report' in data.keys() and data['classification_report']:
print(data['classification_report']) precision recall f1-score support
No Finding 0.11 0.10 0.10 4574
Enlarged Cardiomediastinum 0.06 0.04 0.05 2022
Cardiomegaly 0.13 0.14 0.13 5522
Lung Opacity 0.46 0.18 0.26 20674
Lung Lesion 0.04 0.01 0.02 1709
Edema 0.24 0.18 0.21 10348
Consolidation 0.06 0.01 0.01 2922
Pneumonia 0.03 0.00 0.00 1215
Atelectasis 0.13 0.01 0.02 6714
Pneumothorax 0.08 0.04 0.05 3652
Pleural Effusion 0.39 0.37 0.38 17290
Pleural Other 0.02 0.00 0.01 694
Fracture 0.04 0.01 0.01 1726
Support Devices 0.51 0.52 0.52 23077
micro avg 0.36 0.25 0.29 102139
macro avg 0.16 0.12 0.13 102139
weighted avg 0.33 0.25 0.27 102139
samples avg 0.28 0.23 0.23 102139
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3142490088939667
Test auc: 0.7549559473991394
Test precision: 0.6636328101158142
Test recall: 0.46258530020713806
Test f2_score: 0.49242109060287476
Test binary_accuracy: 0.8729780912399292
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_WBCE_E20_B32',
'dataset_folder': 'data/chexpert/full',
'dataset_name': 'chexpert_full',
'dim': [256, 256],
'drop_last': True,
'epochs': 20,
'label_columns': [ 'No Finding',
'Enlarged Cardiomediastinum',
'Cardiomegaly',
'Lung Opacity',
'Lung Lesion',
'Edema',
'Consolidation',
'Pneumonia',
'Atelectasis',
'Pneumothorax',
'Pleural Effusion',
'Pleural Other',
'Fracture',
'Support Devices'],
'loss': 'binary_crossentropy',
'metrics': ['auc', 'precision', 'recall', 'f2_score', 'binary_accuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'negative_weights': [ 1.110120177268982,
1.051700472831726,
1.1367506980895996,
1.899986743927002,
1.0429342985153198,
1.30768883228302,
1.0704970359802246,
1.0280543565750122,
1.1742662191390991,
1.096386432647705,
1.6260361671447754,
1.015804648399353,
1.0428282022476196,
2.0840952396392822],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 10.080989837646484,
20.34217643737793,
8.312580108642578,
2.1111273765563965,
24.291378021240234,
4.25003719329834,
15.185005187988281,
36.6450309753418,
6.738346099853516,
11.374900817871094,
2.5973517894744873,
64.27252197265625,
24.349124908447266,
1.9224282503128052],
'shuffle': True,
'test_num_samples': 44369,
'train_num_samples': 143410,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': True,
'valid_num_samples': 35635}
if 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
benchmark = data['benchmark']
dataset_path = Path(benchmark['dataset_folder'])
train_labels = benchmark['train_labels'] if 'train_labels' in benchmark.keys() else 'train.csv'
split_test_size = benchmark['split_test_size'] if 'split_test_size' in benchmark.keys() else 0.2
split_valid_size = benchmark['split_valid_size'] if 'split_valid_size' in benchmark.keys() else 0.2
split_group = benchmark['split_group'] if 'split_group' in benchmark.keys() else 'patient_id'
split_seed = benchmark['split_seed']
all_labels = pd.read_csv(dataset_path / train_labels)
train_labels, test_labels = train_test_split(all_labels, test_size=split_test_size, group=split_group, seed=split_seed)
train_labels, validation_labels = train_test_split(train_labels, test_size=split_valid_size, group=split_group, seed=split_seed)from src.datasets.u_encoding import uencode
def get_distribution(labels):
if 'nan_replacement' in benchmark.keys():
labels = labels.fillna(benchmark['nan_replacement'])
data = labels.to_numpy()
data = uencode(benchmark['u_enc'], data, unc_value=benchmark['unc_value'])
data = pd.DataFrame(data, columns=labels.columns)
labels = data[benchmark['label_columns']]
d = {'Pathology': [], 'Positive': [], 'Positive %': [], 'Negative': [], 'Negative %': [],}
for label in labels.columns:
values = labels.groupby(label)
d['Pathology'].append(label)
positive = values.size()[1.0] if 1.0 in values.size() else 0
positive_percent = positive / labels.shape[0] * 100
d['Positive'].append(positive)
d['Positive %'].append(round(positive_percent))
negative = values.size()[-0.0] if -0.0 in values.size() else 0
negative_percent = negative / labels.shape[0] * 100
d['Negative'].append(negative)
d['Negative %'].append(round(negative_percent))
df = pd.DataFrame(d)
df = df.set_index('Pathology')
return dfif 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
train = get_distribution(train_labels)
val = get_distribution(validation_labels)
test = get_distribution(test_labels)
positives = train[['Positive %']].merge(val[['Positive %']], left_index=True, right_index=True).merge(test[['Positive %']], left_index=True, right_index=True).rename(columns={"Positive %_x": "Positives Train", "Positive %_y": "Positives Validation", "Positive %": "Positives Test", })
positives.copy().plot(kind='bar', figsize=(10,7), title="Positive Labels Distribution")
negatives = train[['Negative %']].merge(val[['Negative %']], left_index=True, right_index=True).merge(test[['Negative %']], left_index=True, right_index=True).rename(columns={"Negative %_x": "Negative Train", "Negative %_y": "Negative Validation", "Negative %": "Negative Test", })
negatives.copy().plot(kind='bar', figsize=(10,7), title="Negative Labels Distribution")
train[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Training set")
val[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Validation set")
test[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Test set")