Datasets:
HenryAI
/
KerasBERTv1-Data

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# Split data in the ratio 70-30 for training and validation.
x_train = np.concatenate((abnormal_scans[:70], normal_scans[:70]), axis=0)
y_train = np.concatenate((abnormal_labels[:70], normal_labels[:70]), axis=0)
x_val = np.concatenate((abnormal_scans[70:], normal_scans[70:]), axis=0)
y_val = np.concatenate((abnormal_labels[70:], normal_labels[70:]), axis=0)
print(
\"Number of samples in train and validation are %d and %d.\"
% (x_train.shape[0], x_val.shape[0])
)
Number of samples in train and validation are 140 and 60.
Data augmentation
The CT scans also augmented by rotating at random angles during training. Since the data is stored in rank-3 tensors of shape (samples, height, width, depth), we add a dimension of size 1 at axis 4 to be able to perform 3D convolutions on the data. The new shape is thus (samples, height, width, depth, 1). There are different kinds of preprocessing and augmentation techniques out there, this example shows a few simple ones to get started.
import random
from scipy import ndimage
@tf.function
def rotate(volume):
\"\"\"Rotate the volume by a few degrees\"\"\"
def scipy_rotate(volume):
# define some rotation angles
angles = [-20, -10, -5, 5, 10, 20]
# pick angles at random
angle = random.choice(angles)
# rotate volume
volume = ndimage.rotate(volume, angle, reshape=False)
volume[volume < 0] = 0
volume[volume > 1] = 1
return volume
augmented_volume = tf.numpy_function(scipy_rotate, [volume], tf.float32)
return augmented_volume
def train_preprocessing(volume, label):
\"\"\"Process training data by rotating and adding a channel.\"\"\"
# Rotate volume
volume = rotate(volume)
volume = tf.expand_dims(volume, axis=3)
return volume, label
def validation_preprocessing(volume, label):
\"\"\"Process validation data by only adding a channel.\"\"\"
volume = tf.expand_dims(volume, axis=3)
return volume, label
While defining the train and validation data loader, the training data is passed through and augmentation function which randomly rotates volume at different angles. Note that both training and validation data are already rescaled to have values between 0 and 1.
# Define data loaders.
train_loader = tf.data.Dataset.from_tensor_slices((x_train, y_train))
validation_loader = tf.data.Dataset.from_tensor_slices((x_val, y_val))
batch_size = 2
# Augment the on the fly during training.
train_dataset = (
train_loader.shuffle(len(x_train))
.map(train_preprocessing)
.batch(batch_size)
.prefetch(2)
)
# Only rescale.
validation_dataset = (
validation_loader.shuffle(len(x_val))
.map(validation_preprocessing)
.batch(batch_size)
.prefetch(2)
)
Visualize an augmented CT scan.
import matplotlib.pyplot as plt
data = train_dataset.take(1)
images, labels = list(data)[0]
images = images.numpy()
image = images[0]
print(\"Dimension of the CT scan is:\", image.shape)
plt.imshow(np.squeeze(image[:, :, 30]), cmap=\"gray\")
Dimension of the CT scan is: (128, 128, 64, 1)
<matplotlib.image.AxesImage at 0x7fea680354e0>
png
Since a CT scan has many slices, let's visualize a montage of the slices.
def plot_slices(num_rows, num_columns, width, height, data):
\"\"\"Plot a montage of 20 CT slices\"\"\"
data = np.rot90(np.array(data))
data = np.transpose(data)
data = np.reshape(data, (num_rows, num_columns, width, height))
rows_data, columns_data = data.shape[0], data.shape[1]
heights = [slc[0].shape[0] for slc in data]
widths = [slc.shape[1] for slc in data[0]]
fig_width = 12.0
fig_height = fig_width * sum(heights) / sum(widths)
f, axarr = plt.subplots(
rows_data,