mlsanityData to 7 Oct 2026

Recent advances and clinical applications of deep learning in medical image analysis

arXiv 2105.13381 ↗First posted 27 May 2021cs.CV eess.IVcited by 104

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Abstract (arXiv)

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss the major technical challenges and suggest the possible solutions in future research efforts.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Builds on 225 of its 359 references that are in the map. First posted on arXiv 27 May 2021.

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225 of 359 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 17; 2024: 48; 2025: 19; 2026: 12.

104 papers.

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  31. 2024Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Studycited by 3
  32. 2024Adversarially Robust Feature Learning for Breast Cancer Diagnosiscited by 3
  33. 2024MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learningcited by 3
  34. 2024Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuningcited by 3
  35. 2024Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosiscited by 3
  36. 2024Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?cited by 3
  37. 2024Contour-weighted loss for class-imbalanced image segmentationcited by 3
  38. 2024Med-TTT: Vision Test-Time Training model for Medical Image Segmentationcited by 3

50 most cited of 104 All 104 (JSON)