ReviewNeural computing & applications2023
A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.
Review in Neural computing & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing dermatopathology using AI in skin diagnostics: scoping review.Frontiers in medicine · 2026Pooled it
- The role of infrared thermography in thyroid nodule detection and classification: a scoping review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death.Clinical cardiology · 2026Review
- The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.International journal of computer assisted radiology and surgery · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.Frontiers in digital health · 2026Review
- BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images.Cognitive neurodynamics · 2025Article
- Tracking False Lumen Remodeling with AI: A Variational Autoencoder Approach After Frozen Elephant Trunk Surgery.Journal of personalized medicine · 2025Article
- Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation.Scientific reports · 2025Article
- Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.Dysphagia · 2025Review
- Mechanomedicine for Addressing Skeletal Muscle Insulin Resistance.Endocrine reviews · 2025Review
- Continuous Monitoring with AI-Enhanced BioMEMS Sensors: A Focus on Sustainable Energy Harvesting and Predictive Analytics.Micromachines · 2025Review
- Character generation and visual quality enhancement in animated films using deep learning.Scientific reports · 2025Article
- MAFL-Attack: a targeted attack method against deep learning-based medical image segmentation models.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.Journal of imaging informatics in medicine · 2025Article
- Article
- Generative AI for Diagnostic Medical Imaging: A Review.Current medical imaging · 2025Review
- An Artificial Intelligence-Based Automatic Classifier for the Presence of False Lumen Thrombosis After Frozen Elephant Trunk Operation.Diagnostics (Basel, Switzerland) · 2024Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.