mlsanityData to 14 Oct 2026

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

arXiv 1802.08717 ↗First posted 10 Feb 2018cs.CV cs.LG stat.APcited by 34

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Abstract (arXiv)

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2018.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 3; 2024: 8; 2025: 5; 2026: 1.

34 papers.

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  4. 2022Deep transfer learning for image classification: a surveycited by 13
  5. 2021Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumescited by 12
  6. 2024A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Networkcited by 7
  7. 2024Leveraging Foundation Models for Content-Based Image Retrieval in Radiologycited by 6
  8. 2021Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classificationcited by 5
  9. 2021Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 imagescited by 4
  10. 2021Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultscited by 4
  11. 2022Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Imagescited by 3
  12. 2022Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challengescited by 3
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  4. 2024TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIscited by 3
  5. 2025Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applicationscited by 3
  6. 2025OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretationcited by 3
  7. 2021Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentationcited by 2
  8. 2021Non-binary deep transfer learning for image classificationcited by 2
  9. 2021A survey on deep learning approaches for breast cancer diagnosiscited by 1
  10. 2022Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologiescited by 1
  11. 2022Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patientscited by 1
  12. 2022Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Datasetcited by 1
  13. 2023Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentationcited by 1
  14. 2025Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learningcited by 1
  15. 2022Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysiscited by 0
  16. 2022An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Visioncited by 0
  17. 2022Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Datacited by 0
  18. 2023IODeep: an IOD for the introduction of deep learning in the DICOM standardcited by 0
  19. 2024RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Imagescited by 0
  20. 2025Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Releasecited by 0
  21. 2025A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Imagescited by 0
  22. 2026Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Papercited by 0