ArticleJapanese journal of radiology2020
Introduction to deep learning: minimum essence required to launch a research.
Article in Japanese journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AI-enabled predictive, preventive and personalised oral health management: a lightweight patient-centred model for automated assessment of dental plaque and gingival inflammation.The EPMA journal · 2026Article
- Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach.BMC nursing · 2024Article
- Article
- From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.Analytical and bioanalytical chemistry · 2024Review
- Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance.Japanese journal of radiology · 2023Article
- Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation.Japanese journal of radiology · 2023Article
- Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine.Scientific reports · 2022Article
- Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images.Canadian respiratory journal · 2022Article
- Introduction to Deep Learning in Clinical Neuroscience.Acta neurochirurgica. Supplement · 2022Article
- Psychological Mobilization of Innovative Teaching Methods for Students' Basic Educational Curriculum Reform Under Deep Learning.Frontiers in psychology · 2022Article
- Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.Molecular diversity · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the present article, we provide an overview on the basics of deep learning in terms of technical aspects and steps required to launch a deep learning research. Deep learning is a branch of artificial intelligence, which has been attracting interest in many domains. The essence of deep learning can be compared to teaching an elementary school student how to differentiate magnetic resonance images, and we first explain the concept using this analogy. Deep learning models are composed of many layers including input, hidden, and output ones. Convolutional neural networks are suitable for image processing as convolutional and pooling layers allow successfully performing extraction of image features. The process of conducting a research work with deep learning can be divided into the nine following steps: computer preparation, software installation, specifying the function, data collection, data edits, dataset creation, programming, program execution, and verification of results. Concerning widespread expectations, deep learning cannot be applied to solve tasks other than those set in specification; moreover, it requires a large amount of data to train and has difficulties with recognizing unknown concepts. Deep learning cannot be considered as a universal tool, and researchers should have thorough understanding of the features of this technique.
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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.