A Ortiz

A Rajkomar

BJ Erickson

C Prates

C Salvatore

CJ Herold

D Fehr

D Nie

G Orru

I Beheshti

JA Cruz

JC Weinreb

JPF Sánchez

JZ Cheng

K Lekadir

KH Cha

M Anthimopoulos

M Ghafoorian

Q Zhang

RK Samala

RL Siegel

SM Plis

V Gulshan

VK Ithapu

XW Gao

Y Guo

Scientific Reports

AbstractProstate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 &lt; 0.001). The AUCs were 0.84 (95% CI 0.78–0.89) for deep learning method and 0.70 (95% CI 0.63–0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.</jats:p

Xinggang Wang

Wei Yang

Jeffrey Weinreb

Juan Han

Qiubai Li

Xiangchuang Kong

Yongluan Yan

Zan Ke

Bo Luo

Tao Liu

Liang Wang

Crossref

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

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