ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2021
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 313 papers, 5 of them syntheses that pooled it.
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Who cites it
313 citing papers in PubMed, 5 syntheses or guidelines pooled it.
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- Convolutional neural networks for medical imaging in resource-constrained settings: a scoping review of architectures, performance, and deployment challenges.Frontiers in artificial intelligence · 2026Pooled it
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Epicardial and pericardial adipose tissue: anatomy, physiology, imaging, segmentation, and treatment effects.The British journal of radiology · 2026Review
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- A Systematic Review of the Use of Artificial Intelligence Systems for Assessing Multiple Parameters During the Ultrasound Evaluation of Fetal Growth.Diagnostics (Basel, Switzerland) · 2026Review
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- Metabolic imaging for gamete and embryo assessment through advanced microscopy technologies: a novel avenue for artificial intelligence?Human reproduction (Oxford, England) · 2026Review
- AFFUNet: adaptive feature fusion Transformer U-Net with joint loss function for medical image segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Volumetric reference data of the orbit: a deep learning MRI analysis in the German national cohort.Scientific reports · 2026Article
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis.Journal of applied clinical medical physics · 2026Article
- Advances in Machine Learning-Assisted Optical Sensing Arrays for Disease Diagnosis.Biomimetics (Basel, Switzerland) · 2026Review
- Artificial intelligence for pediatric fracture detection: impact on diagnostic revisions and patient recall rates in a tertiary emergency setting.BMC emergency medicine · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
253 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
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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.