PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation

Author:

Magoulianitis VasileiosORCID,Yang Jiaxin,Yang Yijing,Xue Jintang,Kaneko MasatomoORCID,Cacciamani GiovanniORCID,Abreu AndreORCID,Duddalwar VinayORCID,Kuo C.-C. Jay,Gill Inderbir S.ORCID,Nikias Chrysostomos

Publisher

Elsevier BV

Reference67 articles.

1. Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder;Abraham;Comput. Med. Imaging Graph.,2018

2. Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier;Abraham;Biocybern. Biomed. Eng.,2018

3. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings;Algohary;J. Magn. Reson. Imaging,2018

4. PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images;Armato III;J. Med. Imaging,2018

5. MAPS: A quantitative radiomics approach for prostate cancer detection;Cameron;IEEE Trans. Biomed. Eng.,2015

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