Automated Gleason grading of prostate cancer tissue microarrays via deep learning
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Summary
A deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining achieves pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort.
- Type
- preprint
- Published
- 2018-03-11
- Cited by
- 431
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W2793814878
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51973901
Keywords
Prostate cancer, Medicine, Cohort, Grading (engineering), Tissue microarray
References
- Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
- A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score
- The Cancer Genome Atlas Pan-Cancer Analysis Project
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- PREDICTION OF PROGNOSIS FOR PROSTATIC ADENOCARCINOMA BY COMBINED HISTOLOGICAL GRADING AND CLINICAL STAGING
- Computational Pathology: Challenges and Promises for Tissue Analysis
- A Coefficient of Agreement for Nominal Scales
- Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
- TMARKER: A free software toolkit for histopathological cell counting and staining estimation
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
- Digital imaging in pathology: whole-slide imaging and beyond.
- ImageNet Large Scale Visual Recognition Challenge
- Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer
- KPNA2 Expression Is an Independent Adverse Predictor of Biochemical Recurrence after Radical Prostatectomy
- Rethinking the Inception Architecture for Computer Vision
- Deep Residual Learning for Image Recognition
- Clinical Validation of the 2005 ISUP Gleason Grading System in a Cohort of Intermediate and High Risk Men Undergoing Radical Prostatectomy
- Prostate cancer grading: Gland segmentation and structural features
- Grading of prostatic adenocarcinoma: current state and prognostic implications
Cited by
- Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions.
- A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients
- Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
- Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
- Coupling weak and strong supervision for classification of prostate cancer histopathology images
- [Artificial intelligence and neural networks in urology].
- Artificial Intelligence in Pathology
- Deep-learning-based identification of odontogenic keratocysts in hematoxylin- and eosin-stained jaw cyst specimens
- Diseño y desarrollo de un sistema automático de clasificación de estructuras glandulares en imágenes histológicas de próstata
- Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.
- Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images
- A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides
- Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images
- First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning
- Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
- Artificial intelligence at the intersection of pathology and radiology in prostate cancer.
- Prädiktive Biomarker in der onkologischen Uropathologie
- [Grading of prostate cancer. For a more precise prognosis].
- A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes
- A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology.
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