def add_image_loss(
model: tf.keras.Model,
fixed_image: tf.Tensor,
pred_fixed_image: tf.Tensor,
loss_config: dict,
) -> tf.keras.Model:
"""
Add image dissimilarity loss of ddf into model.
:param model: tf.keras.Model
:param fixed_image: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param pred_fixed_image: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param loss_config: config for loss
"""
if loss_config["dissimilarity"]["image"]["weight"] > 0:
loss_image = tf.reduce_mean(
image_loss.dissimilarity_fn(
y_true=fixed_image,
y_pred=pred_fixed_image,
**loss_config["dissimilarity"]["image"],
))
weighted_loss_image = (loss_image *
loss_config["dissimilarity"]["image"]["weight"])
model.add_loss(weighted_loss_image)
model.add_metric(loss_image,
name="loss/image_dissimilarity",
aggregation="mean")
model.add_metric(
weighted_loss_image,
name="loss/weighted_image_dissimilarity",
aggregation="mean",
)
return model
def add_ddf_loss(model: tf.keras.Model, ddf: tf.Tensor,
loss_config: dict) -> tf.keras.Model:
"""
add regularization loss of ddf into model
:param model: tf.keras.Model
:param ddf: tensor of shape (batch, m_dim1, m_dim2, m_dim3, 3)
:param loss_config: config for loss
"""
loss_reg = tf.reduce_mean(
deform_loss.local_displacement_energy(ddf,
**loss_config["regularization"]))
weighted_loss_reg = loss_reg * loss_config["regularization"]["weight"]
model.add_loss(weighted_loss_reg)
model.add_metric(loss_reg, name="loss/regularization", aggregation="mean")
model.add_metric(weighted_loss_reg,
name="loss/weighted_regularization",
aggregation="mean")
return model
def add_label_loss(
model: tf.keras.Model,
grid_fixed: tf.Tensor,
fixed_label: (tf.Tensor, None),
pred_fixed_label: (tf.Tensor, None),
loss_config: dict,
) -> tf.keras.Model:
"""
Add label dissimilarity loss of ddf into model.
:param model: tf.keras.Model
:param grid_fixed: tensor of shape (f_dim1, f_dim2, f_dim3, 3)
:param fixed_label: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param pred_fixed_label: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param loss_config: config for loss
"""
if fixed_label is not None:
loss_label = tf.reduce_mean(
label_loss.get_dissimilarity_fn(
config=loss_config["dissimilarity"]["label"])(
y_true=fixed_label, y_pred=pred_fixed_label))
weighted_loss_label = (loss_label *
loss_config["dissimilarity"]["label"]["weight"])
model.add_loss(weighted_loss_label)
model.add_metric(loss_label,
name="loss/label_dissimilarity",
aggregation="mean")
model.add_metric(
weighted_loss_label,
name="loss/weighted_label_dissimilarity",
aggregation="mean",
)
# metrics
dice_binary = label_loss.dice_score(y_true=fixed_label,
y_pred=pred_fixed_label,
binary=True)
dice_float = label_loss.dice_score(y_true=fixed_label,
y_pred=pred_fixed_label,
binary=False)
tre = label_loss.compute_centroid_distance(y_true=fixed_label,
y_pred=pred_fixed_label,
grid=grid_fixed)
foreground_label = label_loss.foreground_proportion(y=fixed_label)
foreground_pred = label_loss.foreground_proportion(y=pred_fixed_label)
model.add_metric(dice_binary,
name="metric/dice_binary",
aggregation="mean")
model.add_metric(dice_float,
name="metric/dice_float",
aggregation="mean")
model.add_metric(tre, name="metric/tre", aggregation="mean")
model.add_metric(foreground_label,
name="metric/foreground_label",
aggregation="mean")
model.add_metric(foreground_pred,
name="metric/foreground_pred",
aggregation="mean")
return model