Image and mask normalization in semantic segmentation for cancer - tensorflow

Question

Could someone please help me, in semantic segmentation tasks, should the image and mask be normalized in the batch generator class or only one of them should be normalized?
I'm using the following code to normalize image and mask:
mean_val, std_val = img.mean(), img.std()
img = (img - mean_val)/std_val
for example :
here image and corresponding masks are normalized for the prostate cancer segmentation task
while here only the masks are normalized
which one is the correct practice?
def __getitem__(self,i):
index= self.indexes[i * self.batch_size : (i + 1) * self.batch_size]
X = np.empty((self.batch_size, self.crop_dim[0], self.crop_dim[1],3)).astype(np.uint8)
Y = np.empty((self.batch_size, self.crop_dim[0], self.crop_dim[1],5)).astype(np.uint8)
for i,ID in enumerate(index):
dim= (self.crop_dim[0],self.crop_dim[1])
img=cv2.imread(self.img_list[ID],cv2.COLOR_BGR2RGB)
img = cv2.resize(img,dim)
mask=imageio.imread(self.labels[ID],as_gray=False, pilmode="RGB")
mask = cv2.resize(mask,dim)
mask= create_labels(mask)
# Augement training patches only
if self.augmentation:
sample = self.augmentation(image=img_numpy, mask=mask_numpy)
img_numpy, mask_numpy = sample['image'], sample['mask']
mean_val, std_val = img.mean(), img.std()
img = (img - mean_val)/std_val
mean_val_mask, std_val_mask = mask.mean(), mask.std()
mask= (mask - mean_val_mask)/std_val_mask
X[i,]=img_numpy
Y[i,]=mask_numpy

Answer 1

No!
You do not want to normalize the labels - you want to predict them directly, you do not want the target (per pixel) to change based on the global statistics of the mask.
Why normalizing?
It is common practice to normalize the inputs to a DNN model. This is motivated by the desire to control the "dynamic range" of the activations at different layers. This, in turn, helps the optimization process to converge in a more rapid and stable manner.
You can find an in-depth analysis of this normalization in this excellent paper:
He, K., Zhang, X., Ren, S. and Sun, J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (ICCV 2015).
This rationale does not apply to the labels.