An Introduction to Deep Learing in Image Recognition （1）

Abstract

It has been about a decade since deep learning has attracted attention in the processing and recognition of medical images. Initially, many researchers had the impression that it wouldn't work without a GPU. It was difficult to use GPUs in the first place, so many researchers could have been confused about incorporating GPUs into their research. In recent years, the stability of the development environment, the expansion of the database, and the use of the GPU have become easier, and anyone can create programs using deep learning. This course extracts important contents from the hands-on seminar held at the JAMIT annual meetings, and explains the execution environment of deep learning and a simple program. The basic content is processed based on building a deep learning environment using TensorFlow and Keras. The sample programs are distributed online in Jupyter Notebook format. Part 1 deals with environment construction and image classification by convolutional neural network, Part 2 deals with environment construction using GPU and area extraction from images, and Part 3 deals with unsupervised learning using AutoEncoder.