Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

By D. Fehr, Harini Veeraraghavan, Andreas Wibmer, Tatsuo Gondo, Kazuhiro Matsumoto, Herbert Alberto Vargas, Evis Sala, Hedvig Hricak, J. Deasy. Proceedings of the National Academy of Sciences of the United States of America, 2015

356 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5e9b24541c3229f8499ba0ef30ffc7519b23f44b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Xinggang Wang

Citations in the corpus

• Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning (2017, methodology): “Traditional image recognition methods, such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Decision Trees (DTs), k-nearest neighbors (k-NN), and Adaboost 9–11 , use hand crafted image features, such as texture, shape, density of pixels, and off-shelf cla”