Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions

This publication has 81 references indexed in Scilit:

• Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma
  Discover Oncology, 2024
• Weakly-supervised deep learning model for prostate cancer diagnosis and Gleason grading of histopathology images
  Biomedical Signal Processing and Control, 2024
• TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma
  Computer Methods and Programs in Biomedicine, 2023
• A novel self-learning framework for bladder cancer grading using histopathological images
  Computers in Biology and Medicine, 2021
• Independent real‐world application of a clinical‐grade automated prostate cancer detection system
  The Journal of Pathology, 2021
• Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies
  JAMA Network Open, 2020
• Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading
  Virchows Archiv, 2020
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
  JAMA, 2016
• The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs—Part B: Prostate and Bladder Tumours
  European Urology, 2016
• EAU Guidelines on Renal Cell Carcinoma: 2014 Update
  European Urology, 2015