Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
- 8 January 2020
- journal article
- research article
- Published by Elsevier BV in The Lancet Oncology
- Vol. 21 (2), 222-232
- https://doi.org/10.1016/s1470-2045(19)30738-7
Abstract
No abstract availableThis publication has 25 references indexed in Scilit:
- Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosisScientific Reports, 2016
- Towards grading gleason score using generically trained deep convolutional neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Gleason grade 4 prostate adenocarcinoma patterns: an interobserver agreement study among genitourinary pathologistsHistopathology, 2016
- The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic CarcinomaThe American Journal of Surgical Pathology, 2016
- Mastering the game of Go with deep neural networks and tree searchNature, 2016
- Prostate cancer screening in men aged 50-69 years (STHLM3): a prospective population-based diagnostic studyThe Lancet Oncology, 2015
- Pathologist Workforce in the United States: I. Development of a Predictive Model to Examine Factors Influencing SupplyArchives of Pathology & Laboratory Medicine, 2013
- Improvement of pathology in sub-Saharan AfricaThe Lancet Oncology, 2013
- Complications After Prostate Biopsy: Data From SEER-MedicareJournal of Urology, 2011
- A UK‐based investigation of inter‐ and intra‐observer reproducibility of Gleason grading of prostatic biopsiesHistopathology, 2006