mlsanityData to 7 Oct 2026

An overview of deep learning in medical imaging focusing on MRI

arXiv 1811.10052 ↗First posted 25 Nov 2018cs.CV cs.LG stat.MLcited by 140

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Abstract (arXiv)

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Key facts. Cited by 140 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2018.

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Citing papers per year: 2021: 39; 2022: 27; 2023: 28; 2024: 25; 2025: 17; 2026: 4.

140 papers.

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  10. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  11. 2021Towards Robust Neural Networks via Close-loop Controlcited by 23
  12. 2022Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantificationcited by 20
38 more
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  4. 2021Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray imagescited by 16
  5. 2021Distribution-Free Federated Learning with Conformal Predictionscited by 16
  6. 2021RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Datacited by 14
  7. 2024SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRIcited by 14
  8. 2021TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancercited by 13
  9. 2021Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentationcited by 12
  10. 2021Bounding Information Leakage in Machine Learningcited by 11
  11. 2021A real-time spatiotemporal AI model analyzes skill in open surgical videoscited by 11
  12. 2021A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundationscited by 11
  13. 2024Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detectioncited by 10
  14. 2025Topological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classificationcited by 10
  15. 2022Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcomacited by 9
  16. 2023DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Releasecited by 9
  17. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  18. 2022Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inferencecited by 7
  19. 2021Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesiscited by 6
  20. 2021MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Predictioncited by 6
  21. 2021Efficient Anomaly Detection Using Self-Supervised Multi-Cue Taskscited by 6
  22. 2022Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Imagescited by 6
  23. 2023Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMIcited by 6
  24. 20243D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimescited by 6
  25. 2024Segmenting Medical Images: From UNet to Res-UNet and nnUNetcited by 6
  26. 2025Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learningcited by 6
  27. 2021Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasetscited by 5
  28. 2022NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problemscited by 5
  29. 2022Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training datacited by 5
  30. 2022CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mappingcited by 5
  31. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
  32. 2024Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detectioncited by 5
  33. 2024Yucca: A Deep Learning Framework For Medical Image Analysiscited by 5
  34. 2025Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Modelscited by 5
  35. 2021Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imagingcited by 4
  36. 2021Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic resultscited by 4
  37. 2021A framework for benchmarking uncertainty in deep regressioncited by 4
  38. 2021Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)cited by 4

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