# Loss function decays linearly in segmentation MRI fascia

I am working on a segmentation of MRI images of the thigh. I am trying to segment the fascia, there is a slight imbalance between the background and the mask. I have about 1400 images from 30 patients for training and 200 for validation. I am working with keras. The loss function is combination of weighted cross entropy and dice loss (smooth factor of dice loss = 2)

def combo_loss(y_true, y_pred,alpha=0.6, beta=0.6): # beta before 0.4
return alpha*tf.nn.weighted_cross_entropy_with_logits(y_true, y_pred, pos_weight=beta)+((1-alpha)*dice_coefficient_loss(y_true, y_pred))


When I use a value alpha greater thatn 0.5 (the weighted cross entropy) the loss rapidly decreases during the first epoch. Afterwards if slowly decreases in a linear manner. Why is this happening? What would be a good approach reasonable approach to choose the values of alpha and beta?