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Explainable deep learning models in medical image analysis

arXiv 2005.13799 ↗First posted 28 May 2020cs.CV cs.LG eess.IVcited by 111

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Abstract (arXiv)

Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.

Key facts. Cited by 111 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2020.

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Citing papers per year: 2021: 21; 2022: 30; 2023: 20; 2024: 21; 2025: 13; 2026: 6.

111 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  3. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 91
  4. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  5. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  6. 2021Learning to Synthesize Programs as Interpretable and Generalizable Policiescited by 59
  7. 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
  8. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 48
  9. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  10. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  11. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  12. 2023Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Modelscited by 40
38 more
  1. 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 35
  2. 2021Counterfactual Graphs for Explainable Classification of Brain Networkscited by 33
  3. 2021Explainable AI: current status and future directionscited by 33
  4. 2022Explainable Deep Learning Methods in Medical Image Classification: A Surveycited by 32
  5. 2022Doubly Right Object Recognition: A Why Prompt for Visual Rationalescited by 29
  6. 2022Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?cited by 20
  7. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  8. 2022Learning When to Advise Human Decision Makerscited by 18
  9. 2024Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlookscited by 18
  10. 2022Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoderscited by 17
  11. 2022Attribution-based XAI Methods in Computer Vision: A Reviewcited by 16
  12. 2021Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution Viewcited by 15
  13. 2022ECLAD: Extracting Concepts with Local Aggregated Descriptorscited by 15
  14. 2021Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Modelcited by 14
  15. 2024Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2cited by 13
  16. 2023A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directionscited by 12
  17. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  18. 2021One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Imagescited by 11
  19. 2025HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classificationcited by 10
  20. 2021Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanationcited by 9
  21. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 9
  22. 2022Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcarecited by 9
  23. 2022An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networkscited by 8
  24. 2022DermX: an end-to-end framework for explainable automated dermatological diagnosiscited by 8
  25. 2021Uncertainty aware and explainable diagnosis of retinal diseasecited by 7
  26. 2022Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protectioncited by 6
  27. 2024Accurate Explanation Model for Image Classifiers using Class Association Embeddingcited by 6
  28. 2025AI-Assisted Decision Making with Human Learningcited by 6
  29. 2021Voxel-level Importance Maps for Interpretable Brain Age Estimationcited by 5
  30. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  31. 2023Using generative AI to investigate medical imagery models and datasetscited by 5
  32. 2023Explainable AI applications in the Medical Domain: a systematic reviewcited by 5
  33. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  34. 2023UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagerycited by 4
  35. 2024ECOR: Explainable CLIP for Object Recognitioncited by 4
  36. 2024AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scanscited by 4
  37. 2025UbiQTree: Uncertainty Quantification in XAI with Tree Ensemblescited by 4
  38. 2025X-Ray-CoT: Interpretable Chest X-ray Diagnosis with Vision-Language Models via Chain-of-Thought Reasoningcited by 4

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