DenseNet121_Chexpert_BCE_E20_B32 - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained DenseNet121 architecture using the 'Chexpert_BCE_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. A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 142376 number of sample, the validation set 36704, and the test set 44334.
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_BCE_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.
A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 142376 number of sample, the validation set 36704, and the test set 44334.
# 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 0.09 0.08 0.08 4408
Enlarged Cardiomediastinum 0.04 0.00 0.00 2204
Cardiomegaly 0.10 0.09 0.10 5347
Lung Opacity 0.48 0.54 0.51 21081
Lung Lesion 0.03 0.00 0.01 1772
Edema 0.24 0.14 0.18 10541
Consolidation 0.07 0.01 0.01 2874
Pneumonia 0.19 0.00 0.01 1148
Atelectasis 0.16 0.00 0.01 6700
Pneumothorax 0.09 0.04 0.06 4021
Pleural Effusion 0.39 0.40 0.39 17264
Pleural Other 0.00 0.00 0.00 726
Fracture 0.06 0.01 0.01 1832
Support Devices 0.52 0.55 0.54 23135
micro avg 0.40 0.33 0.36 103053
macro avg 0.18 0.13 0.14 103053
weighted avg 0.34 0.33 0.32 103053
samples avg 0.35 0.30 0.29 103053
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.29399368166923523
Test auc: 0.7812870144844055
Test precision: 0.6907973885536194
Test recall: 0.5565097332000732
Test f2_score: 0.5790215134620667
Test binary_accuracy: 0.8849048614501953
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_BCE_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': [ 1.1137117147445679,
1.0508182048797607,
1.135301947593689,
1.8874187469482422,
1.0435165166854858,
1.301281452178955,
1.0710320472717285,
1.0280910730361938,
1.1757400035858154,
1.094498634338379,
1.6167526245117188,
1.0159780979156494,
1.0420347452163696,
2.074005126953125],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 9.794166564941406,
20.677995681762695,
8.390876770019531,
2.126863718032837,
23.979787826538086,
4.319155216217041,
15.078161239624023,
36.59845733642578,
6.690225601196289,
11.582167625427246,
2.6213955879211426,
63.58552932739258,
24.789831161499023,
1.9310944080352783],
'shuffle': True,
'test_num_samples': 44334,
'train_num_samples': 142376,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': False,
'valid_num_samples': 36704}
Version: 1
Trained DenseNet121 architecture using the 'Chexpert_BCE_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. A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 142376 number of sample, the validation set 36704, and the test set 44334.
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_BCE_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.
A total of 14 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 142376 number of sample, the validation set 36704, and the test set 44334.
# 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 0.09 0.08 0.08 4408
Enlarged Cardiomediastinum 0.04 0.00 0.00 2204
Cardiomegaly 0.10 0.09 0.10 5347
Lung Opacity 0.48 0.54 0.51 21081
Lung Lesion 0.03 0.00 0.01 1772
Edema 0.24 0.14 0.18 10541
Consolidation 0.07 0.01 0.01 2874
Pneumonia 0.19 0.00 0.01 1148
Atelectasis 0.16 0.00 0.01 6700
Pneumothorax 0.09 0.04 0.06 4021
Pleural Effusion 0.39 0.40 0.39 17264
Pleural Other 0.00 0.00 0.00 726
Fracture 0.06 0.01 0.01 1832
Support Devices 0.52 0.55 0.54 23135
micro avg 0.40 0.33 0.36 103053
macro avg 0.18 0.13 0.14 103053
weighted avg 0.34 0.33 0.32 103053
samples avg 0.35 0.30 0.29 103053
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.29399368166923523
Test auc: 0.7812870144844055
Test precision: 0.6907973885536194
Test recall: 0.5565097332000732
Test f2_score: 0.5790215134620667
Test binary_accuracy: 0.8849048614501953
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_BCE_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': [ 1.1137117147445679,
1.0508182048797607,
1.135301947593689,
1.8874187469482422,
1.0435165166854858,
1.301281452178955,
1.0710320472717285,
1.0280910730361938,
1.1757400035858154,
1.094498634338379,
1.6167526245117188,
1.0159780979156494,
1.0420347452163696,
2.074005126953125],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 9.794166564941406,
20.677995681762695,
8.390876770019531,
2.126863718032837,
23.979787826538086,
4.319155216217041,
15.078161239624023,
36.59845733642578,
6.690225601196289,
11.582167625427246,
2.6213955879211426,
63.58552932739258,
24.789831161499023,
1.9310944080352783],
'shuffle': True,
'test_num_samples': 44334,
'train_num_samples': 142376,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': False,
'valid_num_samples': 36704}
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")