# A Gentle Introduction to Deep Learning in Medical Image Processing

arXiv: https://arxiv.org/abs/1810.05401 · HTML: https://mlsanity.com/paper/1810.05401/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 30 of 34)

- [Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems](https://mlsanity.com/paper/2211.01323/) (2022)
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- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation](https://mlsanity.com/paper/2405.17496/) (2024)
- [VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis](https://mlsanity.com/paper/2405.05667/) (2024)
- [WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer](https://mlsanity.com/paper/2207.00400/) (2022)
- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy](https://mlsanity.com/paper/2209.11531/) (2022)
- [EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction](https://mlsanity.com/paper/2403.10695/) (2024)
- [Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness](https://mlsanity.com/paper/2202.01208/) (2022)
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- [MRI Images, Brain Lesions and Deep Learning](https://mlsanity.com/paper/2101.05091/) (2021)
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- [Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning](https://mlsanity.com/paper/2111.11660/) (2021)
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- [DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology](https://mlsanity.com/paper/2507.10250/) (2025)
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- [Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance](https://mlsanity.com/paper/2203.06248/) (2022)
- [AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound](https://mlsanity.com/paper/2207.02392/) (2022)
- [Active Informed Consent to Boost the Application of Machine Learning in Medicine](https://mlsanity.com/paper/2210.08987/) (2022)
- [Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography](https://mlsanity.com/paper/2306.01752/) (2023)
- [Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors](https://mlsanity.com/paper/2311.00682/) (2023)
- [The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains](https://mlsanity.com/paper/2403.18343/) (2024)

## Abstract (arXiv)

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.