InceptionV3_Chexpert_BCE_E10_B128 - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained InceptionV3 architecture using the 'Chexpert BCE E10 B128' benchmark.The benchmark was initialized for the chexpert_full dataset with batch size of 128, 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 143020 number of sample, the validation set 36240, and the test set 44154.
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 InceptionV3 architecture using the 'Chexpert BCE E10 B128' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 128, 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 143020 number of sample, the validation set 36240, and the test set 44154.
# 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 0.08 0.01 0.01 4376
Enlarged Cardiomediastinum 0.00 0.00 0.00 2106
Cardiomegaly 0.13 0.01 0.01 5373
Lung Opacity 0.47 0.51 0.49 20760
Lung Lesion 0.00 0.00 0.00 1760
Edema 0.23 0.11 0.15 10416
Consolidation 0.00 0.00 0.00 2791
Pneumonia 0.00 0.00 0.00 1180
Atelectasis 0.00 0.00 0.00 6532
Pneumothorax 0.11 0.00 0.00 3669
Pleural Effusion 0.39 0.33 0.35 16912
Pleural Other 0.00 0.00 0.00 675
Fracture 0.00 0.00 0.00 1824
Support Devices 0.53 0.55 0.54 22813
micro avg 0.45 0.30 0.36 101187
macro avg 0.14 0.11 0.11 101187
weighted avg 0.32 0.30 0.30 101187
samples avg 0.33 0.27 0.27 101187
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3134183883666992
Test AUC: 0.7099723815917969
Test Precision: 0.6531801223754883
Test Recall: 0.42758455872535706
Test F2Score: 0.45931196212768555
Test BinaryAccuracy: 0.8687735199928284
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 128,
'benchmark_name': 'Chexpert BCE E10 B128',
'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': 44154,
'train_num_samples': 143020,
'u_enc': 'uzeroes',
'unc_value': -1,
'valid_num_samples': 36240}
Version: 1
Trained InceptionV3 architecture using the 'Chexpert BCE E10 B128' benchmark.The benchmark was initialized for the chexpert_full dataset with batch size of 128, 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 143020 number of sample, the validation set 36240, and the test set 44154.
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 InceptionV3 architecture using the 'Chexpert BCE E10 B128' benchmark.
The benchmark was initialized for the chexpert_full dataset with batch size of 128, 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 143020 number of sample, the validation set 36240, and the test set 44154.
# 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 0.08 0.01 0.01 4376
Enlarged Cardiomediastinum 0.00 0.00 0.00 2106
Cardiomegaly 0.13 0.01 0.01 5373
Lung Opacity 0.47 0.51 0.49 20760
Lung Lesion 0.00 0.00 0.00 1760
Edema 0.23 0.11 0.15 10416
Consolidation 0.00 0.00 0.00 2791
Pneumonia 0.00 0.00 0.00 1180
Atelectasis 0.00 0.00 0.00 6532
Pneumothorax 0.11 0.00 0.00 3669
Pleural Effusion 0.39 0.33 0.35 16912
Pleural Other 0.00 0.00 0.00 675
Fracture 0.00 0.00 0.00 1824
Support Devices 0.53 0.55 0.54 22813
micro avg 0.45 0.30 0.36 101187
macro avg 0.14 0.11 0.11 101187
weighted avg 0.32 0.30 0.30 101187
samples avg 0.33 0.27 0.27 101187
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.3134183883666992
Test AUC: 0.7099723815917969
Test Precision: 0.6531801223754883
Test Recall: 0.42758455872535706
Test F2Score: 0.45931196212768555
Test BinaryAccuracy: 0.8687735199928284
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 128,
'benchmark_name': 'Chexpert BCE E10 B128',
'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': 44154,
'train_num_samples': 143020,
'u_enc': 'uzeroes',
'unc_value': -1,
'valid_num_samples': 36240}
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")