DenseNet121_Chexpert_BCE_E5_B32_C1_N5 - TobiasSchmidtDE/DeepL-MedicalImaging GitHub Wiki
Version: 1
Trained DenseNet121 architecture using the 'Chexpert_BCE_E5_B32_C1_N5' benchmark. The benchmark was initialized for the chexpert_preprocessed-256-crop dataset with batch size of 32, shuffle set to True and images rescaled to dimension (256, 256). The training was done for 5 epochs using the Adam optimizer and binary_crossentropy loss. A total of 5 labels/pathologies were included in the training and encoded using the 'uzeroes' method. The traing set included 61164 number of sample, the validation set 15207, and the test set 19047.
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_E5_B32_C1_N5' benchmark.
The benchmark was initialized for the chexpert_preprocessed-256-crop dataset with batch size of 32, shuffle set to True and images rescaled to dimension (256, 256).
The training was done for 5 epochs using the Adam optimizer and binary_crossentropy loss.
A total of 5 labels/pathologies were included in the training and encoded using the 'uzeroes' method.
The traing set included 61164 number of sample, the validation set 15207, and the test set 19047.
# 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
Edema 0.26 0.06 0.09 5201
Atelectasis 0.00 0.00 0.00 2747
Pneumonia 0.00 0.00 0.00 788
Lung Opacity 0.91 1.00 0.96 17423
Consolidation 0.00 0.00 0.00 2717
micro avg 0.88 0.61 0.72 28876
macro avg 0.23 0.21 0.21 28876
weighted avg 0.60 0.61 0.59 28876
samples avg 0.90 0.70 0.76 28876
if 'test' in data.keys() and data['test']:
for score_name, score in data["test"].items():
print('Test {}: '.format(score_name), score)
Test loss: 0.36962762475013733
Test auc: 0.6292253732681274
Test precision: 0.8973305225372314
Test recall: 0.6274414658546448
Test f2_score: 0.6676000952720642
Test binary_accuracy: 0.8652127385139465
pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
pp.pprint(data["benchmark"]){ 'batch_size': 32,
'benchmark_name': 'Chexpert_BCE_E5_B32_C1_N5',
'crop': False,
'dataset_folder': 'data/chexpert/preprocessed-256-crop',
'dataset_name': 'chexpert_preprocessed-256-crop',
'dim': [256, 256],
'drop_last': True,
'epochs': 5,
'label_columns': [ 'Edema',
'Atelectasis',
'Pneumonia',
'Lung Opacity',
'Consolidation'],
'loss': 'binary_crossentropy',
'metrics': ['auc', 'precision', 'recall', 'f2_score', 'binary_accuracy'],
'models_dir': 'models',
'n_channels': 3,
'nan_replacement': 0,
'negative_weights': [ 1.3720439672470093,
1.1646225452423096,
1.0462990999221802,
11.449541091918945,
1.1718534231185913],
'optimizer': 'Adam',
'path_column': 'Path',
'path_column_prefix': '',
'positive_weights': [ 3.687854290008545,
7.074502468109131,
22.598669052124023,
1.0956979990005493,
6.818911552429199],
'shuffle': True,
'split_seed': 6122156,
'test_num_samples': 19047,
'train_num_samples': 61164,
'u_enc': 'uzeroes',
'unc_value': -1,
'use_class_weights': False,
'valid_num_samples': 15207}
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")
