mlsanityData to 7 Oct 2026

Deep neural network models for computational histopathology: A survey

arXiv 1912.12378 ↗First posted 28 Dec 2019eess.IV cs.CV cs.LGcited by 165

Not on the map. Why?

Abstract (arXiv)

Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis. From the survey of over 130 papers, we review the fields progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer learning and various other sub-variants of these methods. We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks. Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.

Key facts. Cited by 165 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 38; 2023: 33; 2024: 25; 2025: 26; 2026: 17.

165 papers.

Sort:

By area:

  1. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  2. 2021BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Imagescited by 130
  3. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 86
  4. 2022A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Imagescited by 74
  5. 2021A Petri Dish for Histopathology Image Analysiscited by 63
  6. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  7. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 58
  8. 2021Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labelscited by 43
  9. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 42
  10. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  11. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  12. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
38 more
  1. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  2. 2022WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinomacited by 32
  3. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  4. 2022ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classificationcited by 30
  5. 2022Towards 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 Analysiscited by 29
  6. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  7. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  8. 2021GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detectioncited by 26
  9. 2021Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathologycited by 25
  10. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  11. 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classificationcited by 21
  12. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 21
  13. 2022Deep Learning for Computational Cytology: A Surveycited by 20
  14. 2022FHIST: A Benchmark for Few-shot Classification of Histological Imagescited by 20
  15. 2024CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignmentcited by 20
  16. 2024Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Predictioncited by 20
  17. 2021Learning to Downsample for Segmentation of Ultra-High Resolution Imagescited by 19
  18. 2022Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningcited by 16
  19. 2024MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuningcited by 16
  20. 2022Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Imagescited by 15
  21. 2023HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Imagecited by 14
  22. 2021DiagSet: a dataset for prostate cancer histopathological image classificationcited by 13
  23. 2021Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendationscited by 13
  24. 2021Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Imagescited by 13
  25. 2023CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Imagescited by 13
  26. 2022A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Predictioncited by 11
  27. 2022ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Imagescited by 11
  28. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  29. 2024Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysiscited by 11
  30. 2022RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapycited by 10
  31. 2022IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classificationcited by 10
  32. 2022Efficient High-Resolution Deep Learning: A Surveycited by 9
  33. 2023Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracycited by 9
  34. 2023Multi-Scale Prototypical Transformer for Whole Slide Image Classificationcited by 9
  35. 2023A Framework for Interpretability in Machine Learning for Medical Imagingcited by 9
  36. 2024Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offscited by 9
  37. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  38. 2026Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Modelscited by 9

50 most cited of 165 All 165 (JSON)