ArticleIEEE transactions on radiation and plasma medical sciences2021
Application and Construction of Deep Learning Networks in Medical Imaging.
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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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Who cites it
14 citing papers in PubMed.
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- Deep atrous context convolution generative adversarial network with corner key point extracted feature for nuts classification.Scientific reports · 2026Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation.Frontiers in neuroinformatics · 2025Article
- Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [European radiology · 2024Article
- Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture.PloS one · 2024Article
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- PHFBioengineering (Basel, Switzerland) · 2022Article
- Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography.Diagnostics (Basel, Switzerland) · 2022Article
- On Interpretability of Artificial Neural Networks: A Survey.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.PET clinics · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems, such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this article reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.
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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.