Related references
Note: Only part of the references are listed.Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Wouter Bulten et al.
NATURE MEDICINE (2022)
A deep learning model for breast ductal carcinoma in situ classification in whole slide images
Fahdi Kanavati et al.
VIRCHOWS ARCHIV (2022)
A Visuoperceptual Measure for Videofluoroscopic Swallow Studies (VMV): A Pilot Study of Validity and Reliability in Adults with Dysphagia
Katina Swan et al.
JOURNAL OF CLINICAL MEDICINE (2022)
Active surveillance in favorable intermediate risk prostate cancer: outstanding questions and controversies
J. Ryan Russell et al.
CURRENT OPINION IN ONCOLOGY (2022)
PathAL: An Active Learning Framework for Histopathology Image Analysis
Wenyuan Li et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)
A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies
Nitin Singhal et al.
SCIENTIFIC REPORTS (2022)
No significant difference in intermediate key outcomes in men with low- and intermediate-risk prostate cancer managed by active surveillance
Karolina Cyll et al.
SCIENTIFIC REPORTS (2022)
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning
Masayuki Tsuneki et al.
DIAGNOSTICS (2022)
Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
Wouter Bulten et al.
MODERN PATHOLOGY (2021)
An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies
Felicia Marginean et al.
EUROPEAN UROLOGY FOCUS (2021)
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Hyuna Sung et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2021)
WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images
Julio Silva-Rodriguez et al.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2021)
Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning
Masayuki Tsuneki et al.
DIAGNOSTICS (2021)
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy
Yoshiki Naito et al.
SCIENTIFIC REPORTS (2021)
Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification
Sebastian Otalora et al.
BMC MEDICAL IMAGING (2021)
Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification
Wei Huang et al.
JAMA NETWORK OPEN (2021)
Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images
Fahdi Kanavati et al.
TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2021)
Detecting and grading prostate cancer in radical prostatectomy specimens through deep learning techniques
Petronio Augusto de Souza Melo et al.
CLINICS (2021)
Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
Wouter Bulten et al.
LANCET ONCOLOGY (2020)
Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours
Osamu Iizuka et al.
SCIENTIFIC REPORTS (2020)
The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma
Geert J. L. H. van Leenders et al.
AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2020)
Weakly-supervised learning for lung carcinoma classification using deep learning
Fahdi Kanavati et al.
SCIENTIFIC REPORTS (2020)
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
Arkadiusz Gertych et al.
SCIENTIFIC REPORTS (2019)
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella et al.
NATURE MEDICINE (2019)
Occurrence of the potent mutagens 2-nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles
Aldenor G. Santos et al.
SCIENTIFIC REPORTS (2019)
Randomized controlled trial of a 12-week digital care program in improving low back pain
Raad Shebib et al.
NPJ DIGITAL MEDICINE (2019)
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Joel Saltz et al.
CELL REPORTS (2018)
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
Nicolas Coudray et al.
NATURE MEDICINE (2018)
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Babak Ehteshami Bejnordi et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis
Xin Luo et al.
JOURNAL OF THORACIC ONCOLOGY (2017)
Accuracy of Grading Gleason Score 7 Prostatic Adenocarcinoma on Needle Biopsy: Influence of Percent Pattern 4 and Other Histological Factors
Abdelrazak Meliti et al.
PROSTATE (2017)
Interobserver Reproducibility of Percent Gleason Pattern 4 in Prostatic Adenocarcinoma on Prostate Biopsies
Evita T. Sadimin et al.
AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2016)
Classifying and segmenting microscopy images with deep multiple instance learning
Oren Z. Kraus et al.
BIOINFORMATICS (2016)
Active Surveillance for the Management of Localized Prostate Cancer (Cancer Care Ontario Guideline): American Society of Clinical Oncology Clinical Practice Guideline Endorsement
Ronald C. Chen et al.
JOURNAL OF CLINICAL ONCOLOGY (2016)
Outcome of Gleason 3+5=8 Prostate Cancer Diagnosed on Needle Biopsy: Prognostic Comparison with Gleason 4+4=8
Nicholas Harding-Jackson et al.
JOURNAL OF UROLOGY (2016)
Image analysis and machine learning in digital pathology: Challenges and opportunities
Anant Madabhushi et al.
MEDICAL IMAGE ANALYSIS (2016)
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Kun-Hsing Yu et al.
NATURE COMMUNICATIONS (2016)
Interobserver variability in Gleason histological grading of prostate cancer
Tayyar A. Ozkan et al.
SCANDINAVIAN JOURNAL OF UROLOGY (2016)
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Geert Litjens et al.
SCIENTIFIC REPORTS (2016)
Diagnosis of Poorly Formed Glands Gleason Pattern 4 Prostatic Adenocarcinoma on Needle Biopsy An Interobserver Reproducibility Study Among Urologic Pathologists With Recommendations
Ming Zhou et al.
AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2015)
Active surveillance for the management of localized prostate cancer: Guideline recommendations
Chris Morash et al.
CUAJ-CANADIAN UROLOGICAL ASSOCIATION JOURNAL (2015)
The Potential Impact of Reproducibility of Gleason Grading in Men With Early Stage Prostate Cancer Managed by Active Surveillance: A Multi-Institutional Study
Jesse K. McKenney et al.
JOURNAL OF UROLOGY (2011)
Interactive digital slides with heat maps: a novel method to improve the reproducibility of Gleason grading
Lars Egevad et al.
VIRCHOWS ARCHIV (2011)
Measurement of observer agreement
HL Kundel et al.
RADIOLOGY (2003)
Interobserver reproducibility of Gleason grading of prostatic carcinoma: General pathologists
WC Allsbrook et al.
HUMAN PATHOLOGY (2001)