Is it possible to have differential weights as per input importance for a Neural Network I have a Computed Tomography data set where central slices are more important as the scan angle approaches 90 degrees. The information in the initial and last slices(0 degrees and 180 degrees) may assigned lesser weight as they contain lateral information. Could anyone please suggest me a way to assign weights as per importance of the input? Shall I focus on any specific hyper-parameter while training?
Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.

Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)

weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)  
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32) 


Happy to answer.