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qc-abide-2d.py
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qc-abide-2d.py
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from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Conv2D, MaxPooling2D, Flatten, BatchNormalization, Input
from keras.callbacks import ModelCheckpoint
from keras.layers.merge import add, concatenate
from keras.optimizers import SGD
import numpy as np
import h5py
import pickle
import keras.backend as K
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit
from custom_loss import sensitivity, specificity
workdir = '/data1/data/deepqc/'
image_size = (192, 256, 192)
slice_size = (192, 256)
def qc_model():
nb_classes = 2
conv_size = (3, 3)
pool_size = (2, 2)
model = Sequential()
model.add(Conv2D(16, conv_size, activation='relu', input_shape=(192, 256, 192)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Conv2D(32, conv_size, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Conv2D(32, conv_size, activation='relu'))
# model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Conv2D(64, conv_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Conv2D(64, conv_size, activation='relu'))
# model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Conv2D(128, conv_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.4))
model.add(Conv2D(256, conv_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
sgd = SGD(lr=1e-3, momentum=0.9, decay=1e-6, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=["accuracy", sensitivity, specificity])
return model
def batch(indices, f):
images = f['MRI']
labels = f['qc_label'] #already in one-hot
while True:
np.random.shuffle(indices)
for index in indices:
try:
# print(images[index, ...][np.newaxis, ...].shape)
yield (images[index, ...][np.newaxis, ...], labels[index, ...][np.newaxis, ...])
except:
yield (images[index, ...][np.newaxis, ...])
def plot_training_error(hist):
epoch_num = range(len(hist.history['acc']))
train_error = np.subtract(1, np.array(hist.history['acc']))
test_error = np.subtract(1, np.array(hist.history['val_acc']))
plt.clf()
plt.plot(epoch_num, train_error, label='Training Error')
plt.plot(epoch_num, test_error, label="Validation Error")
plt.legend(shadow=True)
plt.xlabel("Training Epoch Number")
plt.ylabel("Error")
plt.savefig(workdir + 'results.png')
plt.close()
if __name__ == "__main__":
abide_indices = pickle.load(open(workdir + 'abide_indices.pkl', 'rb'))
ds030_indices = pickle.load(open(workdir + 'ds030_indices.pkl', 'rb'))
f = h5py.File(workdir + 'deepqc.hdf5', 'r')
# ping_indices = list(range(0, ping_end_index))
# abide_indices = list(range(ping_end_index, abide_end_index))
# ibis_indices = list(range(abide_end_index, ibis_end_index))
# ds030_indices = list(range(ibis_end_index, ds030_end_index))
# print('ping:', ping_indices)
# print('abide:', abide_indices)
# print('ibis:', ibis_indices)
# print('ds030', ds030_indices)
# train_indices = ping_indices + abide_indices + ibis_indices
train_indices = abide_indices
# print('PING samples:', len(ping_indices))
# print('ABIDE samples:', len(abide_indices))
# print('IBIS samples:', len(ibis_indices))
# print('training samples:', len(train_indices), len(ping_indices) + len(abide_indices) + len(ibis_indices))
train_labels = np.zeros((len(abide_indices), 2))
print('labels shape:', train_labels.shape)
good_subject_index = 0
for index in train_indices:
label = f['qc_label'][index, ...]
train_labels[good_subject_index, ...] = label
good_subject_index += 1
skf = StratifiedShuffleSplit(n_splits=1, test_size = 0.1)
for train, val in skf.split(train_indices, train_labels):
train_indices = train
validation_indices = val
test_indices = ds030_indices
print('train:', train_indices)
print('test:', test_indices)
# define model
model = qc_model()
# print summary of model
model.summary()
num_epochs = 300
model_checkpoint = ModelCheckpoint( workdir + 'best_qc_model.hdf5',
monitor="val_acc",
save_best_only=True)
hist = model.fit_generator(
batch(train_indices, f),
len(train_indices),
epochs=num_epochs,
callbacks=[model_checkpoint],
validation_data=batch(validation_indices, f),
validation_steps=len(validation_indices),
use_multiprocessing=True
)
model.load_weights(workdir + 'best_qc_model.hdf5')
model.save(workdir + 'qc_model.hdf5')
predicted = []
actual = []
for index in test_indices:
scores = model.test_on_batch(f['MRI'][index, ...][np.newaxis, ...], f['qc_label'][index, ...][np.newaxis, ...])
print(scores)
plot_training_error(hist)