A Gentle Introduction to Deep Learning in Medical Image Processing

TL;DR

This paper provides a comprehensive, accessible overview of deep learning fundamentals and their applications in medical image processing, highlighting recent advances, challenges, and future directions.

Contribution

It offers a foundational introduction to deep learning in medical imaging, combining theoretical insights with practical applications and discussing current limitations and future prospects.

Findings

Deep learning has significantly advanced medical image detection and recognition.

Recent trends include physical simulation and modeling for improved imaging.

Limitations include neglect of prior knowledge leading to implausible results.

Abstract

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.