ResNet152V2_Chexpert_BCE_E10_B8 - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained ResNet152V2 architecture using the 'Chexpert BCE E10 B8' benchmark.The benchmark was initialized for the chexpert_full dataset with batch size of 8, shuffel set to True and images rescaled to dimension (256, 256). The training was done for 10 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 142985 number of sample, the validation set 35749, and the test set 44680.
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 ResNet152V2 architecture using the 'Chexpert BCE E10 B8' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 8, shuffel set to True and images rescaled to dimension (256, 256).
The training was done for 10 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 142985 number of sample, the validation set 35749, and the test set 44680.
# 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 0.02 0.03 4506
Enlarged Cardiomediastinum 0.00 0.00 0.00 2152
Cardiomegaly 0.11 0.02 0.04 5458
Lung Opacity 0.47 0.45 0.46 20997
Lung Lesion 0.00 0.00 0.00 1962
Edema 0.24 0.13 0.17 10679
Consolidation 0.00 0.00 0.00 2913
Pneumonia 0.00 0.00 0.00 1251
Atelectasis 0.00 0.00 0.00 6643
Pneumothorax 0.10 0.01 0.02 3949
Pleural Effusion 0.38 0.37 0.38 17209
Pleural Other 0.00 0.00 0.00 679
Fracture 0.13 0.00 0.00 1945
Support Devices 0.51 0.56 0.54 23037
micro avg 0.43 0.29 0.35 103380
macro avg 0.15 0.11 0.12 103380
weighted avg 0.31 0.29 0.30 103380
samples avg 0.33 0.27 0.26 103380
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3126594126224518
Test AUC: 0.7159357070922852
Test Precision: 0.6541891098022461
Test Recall: 0.44426387548446655
Test F2Score: 0.4747315049171448
Test BinaryAccuracy: 0.8693411946296692
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 8,
'benchmark_name': 'Chexpert BCE E10 B8',
'dataset_folder': 'data/chexpert/full',
'dataset_name': 'chexpert_full',
'dim': [256, 256],
'drop_last': True,
'epochs': 10,
'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', 'F2Score', 'BinaryAccuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'shuffle': True,
'test_num_samples': 44680,
'train_num_samples': 142985,
'u_enc': 'uzeroes',
'unc_value': -1,
'valid_num_samples': 35749}
Version: 1
Trained ResNet152V2 architecture using the 'Chexpert BCE E10 B8' benchmark.The benchmark was initialized for the chexpert_full dataset with batch size of 8, shuffel set to True and images rescaled to dimension (256, 256). The training was done for 10 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 142985 number of sample, the validation set 35749, and the test set 44680.
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 ResNet152V2 architecture using the 'Chexpert BCE E10 B8' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 8, shuffel set to True and images rescaled to dimension (256, 256).
The training was done for 10 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 142985 number of sample, the validation set 35749, and the test set 44680.
# 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 0.02 0.03 4506
Enlarged Cardiomediastinum 0.00 0.00 0.00 2152
Cardiomegaly 0.11 0.02 0.04 5458
Lung Opacity 0.47 0.45 0.46 20997
Lung Lesion 0.00 0.00 0.00 1962
Edema 0.24 0.13 0.17 10679
Consolidation 0.00 0.00 0.00 2913
Pneumonia 0.00 0.00 0.00 1251
Atelectasis 0.00 0.00 0.00 6643
Pneumothorax 0.10 0.01 0.02 3949
Pleural Effusion 0.38 0.37 0.38 17209
Pleural Other 0.00 0.00 0.00 679
Fracture 0.13 0.00 0.00 1945
Support Devices 0.51 0.56 0.54 23037
micro avg 0.43 0.29 0.35 103380
macro avg 0.15 0.11 0.12 103380
weighted avg 0.31 0.29 0.30 103380
samples avg 0.33 0.27 0.26 103380
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3126594126224518
Test AUC: 0.7159357070922852
Test Precision: 0.6541891098022461
Test Recall: 0.44426387548446655
Test F2Score: 0.4747315049171448
Test BinaryAccuracy: 0.8693411946296692
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 8,
'benchmark_name': 'Chexpert BCE E10 B8',
'dataset_folder': 'data/chexpert/full',
'dataset_name': 'chexpert_full',
'dim': [256, 256],
'drop_last': True,
'epochs': 10,
'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', 'F2Score', 'BinaryAccuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'shuffle': True,
'test_num_samples': 44680,
'train_num_samples': 142985,
'u_enc': 'uzeroes',
'unc_value': -1,
'valid_num_samples': 35749}
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")