In this blog post I discuss talking (or chatting) to your home using a Telegram bot. I cover making a new Telegram bot, connecting it to Home Assistant and then building functionality using AppDaemon.
Prostate cancer is graded based on distinctive patterns in the tissue. At MIDL2018 I presented an unsupervised deep learning method, based on clustering adversarial autoencoders, to train a system to detect prostate cancer without using labeled data.
Building systems to detect tumor, in this case prostate cancer, is often hard due to a lack of data. Tumor annotations made by pathologists are often coarse due to time constraints. With this project we want to automatically refine these annotations by building a system that can automatically filter out irrelevant parts of the data.
In this post I will show you how to build a custom wake-up light system. The automation will be configurable (on/off, time) and has a switch to disable it on weekends.
In this post we build upon part 1 of 'Getting started with generative adversarial networks' and work with RGB data instead of monochrome. We apply a simple technique to map MNIST images to RGB.