mlsanityData to 7 Oct 2026

Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer

arXiv 1811.06497 ↗First posted 15 Nov 2018cs.CV cs.LGcited by 34

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Abstract (arXiv)

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established by genitourinary specialist pathologists. On the validation dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Nov 2018.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 9; 2024: 4; 2025: 4; 2026: 1.

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  2. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  3. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  4. 2026MedGemma 1.5 Technical Reportcited by 43
  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
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  7. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  8. 2023A Foundational Multimodal Vision Language AI Assistant for Human Pathologycited by 19
  9. 2021Self-learning for weakly supervised Gleason grading of local patternscited by 14
  10. 2021DiagSet: a dataset for prostate cancer histopathological image classificationcited by 13
  11. 2023Domain-specific optimization and diverse evaluation of self-supervised models for histopathologycited by 11
  12. 2022OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histologycited by 9
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  1. 2022Recommendations on test datasets for evaluating AI solutions in pathologycited by 8
  2. 2021Hybrid Supervision Learning for Pathology Whole Slide Image Classificationcited by 5
  3. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  4. 2022Augment like there's no tomorrow: Consistently performing neural networks for medical imagingcited by 5
  5. 2021Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytologycited by 3
  6. 2023Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classificationcited by 2
  7. 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
  8. 2022End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathologycited by 1
  9. 2023A comprehensive review of deep learning in lung cancercited by 1
  10. 2024Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labelscited by 1
  11. 2024HistoEncoder: a digital pathology foundation model for prostate cancercited by 1
  12. 2025Efficient Self-Supervised Grading of Prostate Cancer Pathologycited by 1
  13. 20213D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRIcited by 0
  14. 2022Future Artificial Intelligence tools and perspectives in medicinecited by 0
  15. 2022A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathologycited by 0
  16. 2023The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detectioncited by 0
  17. 2023Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathologycited by 0
  18. 2023Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Reviewcited by 0
  19. 2024Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancercited by 0
  20. 2024Enhanced Cascade Prostate Cancer Classifier in mp-MRI Utilizing Recall Feedback Adaptive Loss and Prior Knowledge-Based Feature Extractioncited by 0
  21. 2025Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Datacited by 0
  22. 2025Hyperparameter Optimization and Reproducibility in Deep Learning Model Trainingcited by 0