Introduction to deep learning: minimum essence required to launch a research

• DOI: 10.1007/s11604-020-00998-2
• Publication Date: 2020-06-17T20:03:31Z
• Authors: Tomohiro Wataya; Katsuyuki Nakanishi; Yuki Suzuki; Shoji Kido; Noriyuki Tomiyama
• Keywords: Diagnostic Imaging; Machine Learning; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Artificial Intelligence; 4. Education; 0202 electrical engineering, electronic engineering, information engineering; Humans; 02 engineering and technology

In the present article, we provide an overview on the basics of deep learning in terms of technical aspects and steps required to launch a deep learning research. Deep learning is a branch of artificial intelligence, which has been attracting interest in many domains. The essence of deep learning can be compared to teaching an elementary school student how to differentiate magnetic resonance images, and we first explain the concept using this analogy. Deep learning models are composed of many layers including input, hidden, and output ones. Convolutional neural networks are suitable for image processing as convolutional and pooling layers allow successfully performing extraction of image features. The process of conducting a research work with deep learning can be divided into the nine following steps: computer preparation, software installation, specifying the function, data collection, data edits, dataset creation, programming, program execution, and verification of results. Concerning widespread expectations, deep learning cannot be applied to solve tasks other than those set in specification; moreover, it requires a large amount of data to train and has difficulties with recognizing unknown concepts. Deep learning cannot be considered as a universal tool, and researchers should have thorough understanding of the features of this technique.