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A Gentle Introduction to Deep Learning in Medical Image Processing

arXiv 1810.05401 ↗First posted 12 Oct 2018cs.CVcited by 34

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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.

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

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Citing papers per year: 2021: 6; 2022: 11; 2023: 4; 2024: 8; 2025: 1; 2026: 4.

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  1. 2022Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systemscited by 44
  2. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  3. 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
  4. 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
  5. 2024UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentationcited by 15
  6. 2024VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesiscited by 8
  7. 2022WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layercited by 7
  8. 2023Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Imagescited by 7
  9. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
  10. 2022Medical image registration using unsupervised deep neural network: A scoping literature reviewcited by 5
  11. 2022Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacycited by 5
  12. 2024EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstructioncited by 5
22 more
  1. 2022Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustnesscited by 3
  2. 2024An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentationcited by 3
  3. 2022Quantum Machine Learning Applied to the Classification of Diabetescited by 2
  4. 2023UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNNcited by 2
  5. 2021MRI Images, Brain Lesions and Deep Learningcited by 1
  6. 2021FlexParser -- the adaptive log file parser for continuous results in a changing worldcited by 1
  7. 2021Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learningcited by 1
  8. 2021Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? -- A Case Study using Dual Energy CT Datacited by 1
  9. 2022Analytic Learning of Convolutional Neural Network For Pattern Recognitioncited by 1
  10. 2024Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectivescited by 1
  11. 2025DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathologycited by 1
  12. 2026Beating the Style Detector: Three Hours of Agentic Research on the AI-Text Arms Racecited by 1
  13. 2022Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performancecited by 0
  14. 2022AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasoundcited by 0
  15. 2022Active Informed Consent to Boost the Application of Machine Learning in Medicinecited by 0
  16. 2023Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiographycited by 0
  17. 2023Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectorscited by 0
  18. 2024The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chainscited by 0
  19. 2024Behavior Backdoor for Deep Learning Modelscited by 0
  20. 2026Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMscited by 0
  21. 2026Agentic Autoresearch for CT Reconstructioncited by 0
  22. 2026Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentationcited by 0