A deep learning network for Gleason grading of prostate biopsies using EfficientNet

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

(2021) Albert Comelli et al.   Applied Sciences-Basel

WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images

(2021) Julio Silva-Rodríguez et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization

(2021) Sushma Shrestha et al.   MULTIMEDIA TOOLS AND APPLICATIONS

Democratising deep learning for microscopy with ZeroCostDL4Mic

(2021) Lucas von Chamier et al.   Nature Communications

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

(2021) Sebastian Otálora et al.   BMC MEDICAL IMAGING

Unexpected long-term survival in an adult patient with metastatic prostate cancer

(2021) P. Antonov et al.   Urology Case Reports

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification

(2021) Niccolò Marini et al.   MEDICAL IMAGE ANALYSIS

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach

(2020) Adeel Ahmed Abbasi et al.   Cognitive Neurodynamics

First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

(2019) Gabriel García et al.   Entropy

Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label‐free multiphoton microscopic images

(2019) Qinqin Yang et al.   Journal of Biophotonics

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies

(2018) Lal Hussain et al.   Cancer Biomarkers

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

(2018) Eirini Arvaniti et al.   Scientific Reports

Prediction of prostate cancer by deep learning with multilayer artificial neural network

(2018) Takumi Takeuchi et al.   CUAJ-Canadian Urological Association Journal

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

(2018) Ruba Alkadi et al.   JOURNAL OF DIGITAL IMAGING

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

(2018) Wenyuan Li et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Prevention of Prostate Cancer Morbidity and Mortality

(2017) Michael J. Barry et al.   MEDICAL CLINICS OF NORTH AMERICA

A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients

(2017) Qing Zhong et al.   Scientific Data

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

(2015) Arkadiusz Gertych et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012

(2014) Jacques Ferlay et al.   INTERNATIONAL JOURNAL OF CANCER

Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification

(2013) Lena Gorelick et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

The Impact of Prostate Volume, Number of Biopsy Cores and American Urological Association Symptom Score on the Sensitivity of Cancer Detection Using the Prostate Cancer Prevention Trial Risk Calculator

(2013) Donna P. Ankerst et al.   JOURNAL OF UROLOGY

Prostate cancer grading: Gland segmentation and structural features

(2011) Kien Nguyen et al.   PATTERN RECOGNITION LETTERS

High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models

(2010) James P. Monaco et al.   MEDICAL IMAGE ANALYSIS

Automatic Classification for Pathological Prostate Images Based on Fractal Analysis

(2009) Po-Whei Huang et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image

(2008) Seok Min Han et al.   JOURNAL OF DIGITAL IMAGING