mlsanityData to 14 Oct 2026

Deep Learning for Medical Image Processing: Overview, Challenges and Future

arXiv 1704.06825 ↗First posted 22 Apr 2017cs.CVcited by 104

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Abstract (arXiv)

Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage of budget. Mostly the interpretations of medical data is being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, the complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health secotr. In this chapter, we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue.

Key facts. Cited by 104 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2017.

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Citing papers per year: 2021: 16; 2022: 18; 2023: 30; 2024: 20; 2025: 13; 2026: 7.

104 papers.

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  1. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  2. 2024Incremental Residual Concept Bottleneck Modelscited by 43
  3. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  4. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  5. 2022Few-shot Medical Image Segmentation with Cycle-resemblance Attentioncited by 33
  6. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 33
  7. 2023MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosiscited by 33
  8. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 30
  9. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  10. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  11. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  12. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 23
38 more
  1. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  2. 2022FHIST: A Benchmark for Few-shot Classification of Histological Imagescited by 20
  3. 2021A survey on shape-constraint deep learning for medical image segmentationcited by 16
  4. 2023Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedbackcited by 15
  5. 2023Point2SSM: Learning Morphological Variations of Anatomies from Point Cloudcited by 14
  6. 2022Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysiscited by 13
  7. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  8. 2021Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledgecited by 12
  9. 2021A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticitycited by 12
  10. 2024Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labelingcited by 12
  11. 2021Stabilized Medical Image Attackscited by 10
  12. 2022Delving into the Openness of CLIPcited by 10
  13. 2022Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languagescited by 10
  14. 2022Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcomacited by 9
  15. 2023Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Frameworkcited by 9
  16. 2024The Pitfalls and Promise of Conformal Inference Under Adversarial Attackscited by 8
  17. 2021DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysiscited by 7
  18. 2023Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Imagescited by 7
  19. 2025MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentationcited by 6
  20. 2023CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasetscited by 5
  21. 2023CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationcited by 5
  22. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  23. 2023FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classificationcited by 4
  24. 2023Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Reviewcited by 4
  25. 2023Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAMcited by 4
  26. 2021Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learningcited by 3
  27. 2022Guiding Machine Perception with Psychophysicscited by 3
  28. 2024Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Studycited by 3
  29. 2024Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistencycited by 3
  30. 2025UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scalecited by 3
  31. 2021Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Imagescited by 2
  32. 2022Suggestive Annotation of Brain MR Images with Gradient-guided Samplingcited by 2
  33. 2023Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classificationcited by 2
  34. 2023UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNNcited by 2
  35. 2024Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodologycited by 2
  36. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  37. 2025Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classificationcited by 2
  38. 2025See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosiscited by 2

50 most cited of 104 All 104 (JSON)