Computer Vision News - June 2020

# impo rt the librar ies import skimage.io as sio import numpy as n p import matplotlib.pyplot as plt %matplotlib inline plt.set_cmap('gray') import torch import torch.nn as nn from t orch.autog rad import Variable import torch.nn.functional as F import os import time 2 Summary Co puter Vision Tools 32 Introduction Th e proposed end-to-end convolutional network model combines a segmentation model with a classification model. The segmentation model is inspired by the fully convolutional networks (FCN) and it can create intermediate pixelwise semantic segmentation maps. Image and semantic segmentation maps feed to the next classification model, in order to designate the requested category. To classify every pixel in each image, the classification network must be "rolled" over the image with a stride 1. Here is the code to get started: Thinking that most of us still enjoy (?) the lockdown by getting creative, learning and working (hopefully!), this month I decided to do with you a practical example of a pixel-wise classification of liver slices. A few of the technologies presented here are PyTorch, pixel-wise classification deep-learning networks and ‘rolling’ the network using hyper-parameters such as stride. PyTorch and Pixel-Wise Liver Slice Classification by Ioannis Valasakis This should include all the required libraries. It is suggested to use a means of isolating your environment such as virtualenv or even conda for this purpose.