mlsanityData to 14 Oct 2026

Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis

arXiv 2208.08789 ↗First posted 18 Aug 2022cs.CVcited by 29

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Abstract (arXiv)

Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome. In recent years, computer-automated analysis techniques for histopathological images have been urgently required in clinical practice, and deep learning methods represented by convolutional neural networks have gradually become the mainstream in the field of digital pathology. However, obtaining large numbers of fine-grained annotated data in this field is a very expensive and difficult task, which hinders the further development of traditional supervised algorithms based on large numbers of annotated data. More recent studies have started to liberate from the traditional supervised paradigm, and the most representative ones are the studies on weakly supervised learning paradigm based on weak annotation, semi-supervised learning paradigm based on limited annotation, and self-supervised learning paradigm based on pathological image representation learning. These new methods have led a new wave of automatic pathological image diagnosis and analysis targeted at annotation efficiency. With a survey of over 130 papers, we present a comprehensive and systematic review of the latest studies on weakly supervised learning, semi-supervised learning, and self-supervised learning in the field of computational pathology from both technical and methodological perspectives. Finally, we present the key challenges and future trends for these techniques.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 89 of its 269 references that are in the map. First posted on arXiv 18 Aug 2022.

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89 of 269 references.

References · 89

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38 more
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50 most cited of 89 All 89 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 6; 2025: 10; 2026: 2.

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  1. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
  2. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 43
  3. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  4. 2023A comprehensive survey on deep active learning in medical image analysiscited by 35
  5. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 22
  6. 2023Unsupervised Keypoints from Pretrained Diffusion Modelscited by 15
  7. 2024Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classificationcited by 13
  8. 2023Hierarchical discriminative learning improves visual representations of biomedical microscopycited by 12
  9. 2025MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysiscited by 7
  10. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  11. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  12. 2023OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classificationcited by 5
17 more
  1. 2025A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanomacited by 4
  2. 2025Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignmentcited by 4
  3. 2023Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentationcited by 3
  4. 2023GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentationcited by 3
  5. 2025Sequential Attention-based Sampling for Histopathological Analysiscited by 2
  6. 2024Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Imagescited by 1
  7. 2024Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approachcited by 1
  8. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  9. 2025Efficient Self-Supervised Grading of Prostate Cancer Pathologycited by 1
  10. 2024PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixingcited by 0
  11. 2024Exploring learning environments for label-efficient cancer diagnosiscited by 0
  12. 2025MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classificationcited by 0
  13. 2025Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervisioncited by 0
  14. 2025Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learningcited by 0
  15. 2025SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Imagescited by 0
  16. 2026Adaptive Prototype-based Interpretable Grading of Prostate Cancercited by 0
  17. 2026Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentationcited by 0