With recent advancements in the development of artificial intelligence applications using theories and algorithms in machine learning, many accurate models can be created to train and predict on given datasets. With the realization of the importance of imaging interpretation in cancer diagnosis, this article aims to investigate the theory behind Deep Learning and Bayesian Network prediction models. Based on the advantages and drawbacks of each model, different approaches will be used to construct a Bayesian Deep Learning Model, combining the strengths while minimizing the weaknesses. Finally, the applications and accuracy of the resulting Bayesian Deep Learning approach in the health industry in classifying images will be analyzed.

It's well known across the machine learning community that choosing the right prior -- an initial belief re an event expressed in terms of a probability distribution -- is crucial for Bayesian inference. Many recent Bayesian deep learning models however resort to established but uninformative or weak informative priors that may have detrimental consequences on their models' inference abilities. In the paper Priors in Bayesian Deep Learning: A Review, a research team from ETH Zürich presents an overview of different priors for (deep) Gaussian processes, variational autoencoders, and Bayesian neural networks. The team proposes that well-chosen priors can actually achieve theoretical and empirical properties such as uncertainty estimation, model selection and optimal decision support; and provides guidance on how to choose them. The main idea of Bayesian models is to infer a posterior distribution over the parameters of a model based on a prior probability for some observed data.

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. I will then cover two techniques for including uncertainty in a deep learning model and will go over a specific example using Keras to train fully connected layers over a frozen ResNet50 encoder on the cifar10 dataset. With this example, I will also discuss methods of exploring the uncertainty predictions of a Bayesian deep learning classifier and provide suggestions for improving the model in the future. This post is based on material from two blog posts (here and here) and a white paper on Bayesian deep learning from the University of Cambridge machine learning group. If you want to learn more about Bayesian deep learning after reading this post, I encourage you to check out all three of these resources. Thank you to the University of Cambridge machine learning group for your amazing blog posts and papers. Bayesian statistics is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief. The combination of Bayesian statistics and deep learning in practice means including uncertainty in your deep learning model predictions. The idea of including uncertainty in neural networks was proposed as early as 1991.