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Automated Gleason Grading of Prostate Biopsies using Deep Learning

arXiv 1907.07980 ↗First posted 18 Jul 2019eess.IV cs.CVcited by 69

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Abstract (arXiv)

The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists. The system has the potential to improve prostate cancer prognostics by acting as a first or second reader.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2019.

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Citing papers per year: 2021: 18; 2022: 9; 2023: 19; 2024: 7; 2025: 10; 2026: 6.

69 papers.

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  1. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 132
  2. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  3. 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
  4. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  5. 2021A Petri Dish for Histopathology Image Analysiscited by 63
  6. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  7. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  8. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  9. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  10. 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classificationcited by 38
  11. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  12. 2023CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Countingcited by 33
38 more
  1. 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
  2. 2025Evidence-based diagnostic reasoning with multi-agent copilot for human pathologycited by 26
  3. 2023A Foundational Multimodal Vision Language AI Assistant for Human Pathologycited by 19
  4. 2024Towards Large-Scale Training of Pathology Foundation Modelscited by 19
  5. 2024PLUTO: Pathology-Universal Transformercited by 17
  6. 2021Self-learning for weakly supervised Gleason grading of local patternscited by 14
  7. 2022Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classificationcited by 14
  8. 2024Learning biologically relevant features in a pathology foundation model using sparse autoencoderscited by 14
  9. 2021DiagSet: a dataset for prostate cancer histopathological image classificationcited by 13
  10. 2021Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRIcited by 11
  11. 2023The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissuecited by 11
  12. 2023Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancercited by 10
  13. 2021WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Imagescited by 9
  14. 2021On the Efficiency of Subclass Knowledge Distillation in Classification Taskscited by 4
  15. 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
  16. 2023Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide imagescited by 4
  17. 2023Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosiscited by 4
  18. 2024T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imagingcited by 4
  19. 2022Calibrating Histopathology Image Classifiers using Label Smoothingcited by 3
  20. 2022Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slidescited by 3
  21. 2023GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentationcited by 3
  22. 2025Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancercited by 3
  23. 2026Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learningcited by 3
  24. 2021Using deep learning to detect patients at risk for prostate cancer despite benign biopsiescited by 2
  25. 2021Bridging the gap between prostate radiology and pathology through machine learningcited by 2
  26. 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingcited by 2
  27. 2023Increasing the usefulness of already existing annotations through WSI registrationcited by 2
  28. 2025PLUTO-4: Frontier Pathology Foundation Modelscited by 2
  29. 2025The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern populationcited by 2
  30. 2021Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systemscited by 1
  31. 2021Code-free development and deployment of deep segmentation models for digital pathologycited by 1
  32. 2021Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Networkcited by 1
  33. 2021Automatic tumour segmentation in H&E-stained whole-slide images of the pancreascited by 1
  34. 2025Efficient Self-Supervised Grading of Prostate Cancer Pathologycited by 1
  35. 2025A Unified Low-level Foundation Model for Enhancing Pathology Image Qualitycited by 1
  36. 2025Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohortcited by 1
  37. 2021Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regressioncited by 0
  38. 2021Automated risk classification of colon biopsies based on semantic segmentation of histopathology imagescited by 0

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