mlsanityData to 7 Oct 2026

Medical Image Analysis using Convolutional Neural Networks: A Review

arXiv 1709.02250 ↗First posted 4 Sep 2017cs.CVcited by 99

Not on the map. Why?

Abstract (arXiv)

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 19; 2023: 27; 2024: 16; 2025: 15; 2026: 7.

99 papers.

Sort:

By area:

  1. 2022Efficient Test-Time Model Adaptation without Forgettingcited by 467
  2. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  3. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  4. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  5. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  6. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  7. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
  8. 2024M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Expertscited by 26
  9. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)cited by 24
  10. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  11. 2023Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classificationcited by 21
  12. 2024Uncertainty-Calibrated Test-Time Model Adaptation without Forgettingcited by 20
38 more
  1. 2022Inflation of test accuracy due to data leakage in deep learning-based classification of OCT imagescited by 17
  2. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  3. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  4. 2023Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detectorcited by 10
  5. 2023ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniquescited by 10
  6. 2021SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadatacited by 9
  7. 2023FineEHR: Refine Clinical Note Representations to Improve Mortality Predictioncited by 9
  8. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 9
  9. 2022SeATrans: Learning Segmentation-Assisted diagnosis model via Transformercited by 8
  10. 2023Bayesian neural networks via MCMC: a Python-based tutorialcited by 8
  11. 2023Calibration of Neural Networkscited by 7
  12. 2022SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRIcited by 6
  13. 2023Complex Mixer for MedMNIST Classification Decathloncited by 6
  14. 2021Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Modelscited by 5
  15. 2022Modern Views of Machine Learning for Precision Psychiatrycited by 5
  16. 2022Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Imagescited by 5
  17. 2023Influencer Backdoor Attack on Semantic Segmentationcited by 5
  18. 2023DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rayscited by 5
  19. 2024Tight Verification of Probabilistic Robustness in Bayesian Neural Networkscited by 5
  20. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  21. 2021Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI datacited by 4
  22. 2021Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Datacited by 4
  23. 2022Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentationcited by 4
  24. 2024LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networkscited by 4
  25. 2025UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentationcited by 4
  26. 2025Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detectioncited by 4
  27. 2021A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Imagescited by 3
  28. 2022Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detectioncited by 3
  29. 2023Class Attention to Regions of Lesion for Imbalanced Medical Image Recognitioncited by 3
  30. 2023A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networkscited by 3
  31. 2023Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applicationscited by 3
  32. 2023SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detectioncited by 3
  33. 2022Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Networkcited by 2
  34. 2023CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological imagescited by 2
  35. 2023FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentationcited by 2
  36. 2024Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methodscited by 2
  37. 2025We Care Each Pixel: Calibrating on Medical Segmentation Modelcited by 2
  38. 2025See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosiscited by 2

50 most cited of 99 All 99 (JSON)