Medical Image Analysis with Deep Learning — I

Note: This is a 4 part article and you can find the other articles via these links (part 1, part 2, part 3, part 4). I have also put together collection of these in a small booklet available via amazon, if you would like a physical copy. Please reach out to me if you have feedback to improve and provide this information to all.

Analyzing images and videos, and using them in various applications such as self driven cars, drones etc. with underlying deep learning techniques has been the new research frontier. The recent research papers such as “A Neural Algorithm of Artistic Style”, show how a styles can be transferred from an artist and applied to an image, to create a new image. Other papers such as “Generative Adversarial Networks” (GAN) and “Wasserstein GAN” have paved the path to develop models that can learn to create data that is similar to data that we give them. Thus opening up the world to semi-supervised learning and paving the path to a future of unsupervised learning.

While these research areas are still on the generic images, our goal is to use these research into medical images to help healthcare. We need to start with some basics. In this article, I start with basics of image processing, basics of medical image format data and visualize some medical data. In the next article I will deep dive into some convolutional neural nets…