Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation

Author:

Yan WenORCID,Chiu Bernard,Shen Ziyi,Yang QianyeORCID,Syer TomORCID,Min Zhe,Punwani Shonit,Emberton MarkORCID,Atkinson DavidORCID,Barratt Dean C.ORCID,Hu YipengORCID

Funder

Cancer Research UK

University of Cambridge

Wellcome Trust

University College London

University of Manchester

Knight Cancer Institute, Oregon Health and Science University

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference67 articles.

1. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study;Ahmed;Lancet,2017

2. Multi-scoring feature selection method based on SVM-RFE for prostate cancer diagnosis;Albashish,2015

3. Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists;Antonelli;Eur. Radiol.,2019

4. Classifier fusion for SVM-based multimedia semantic indexing;Ayache,2007

5. Hyper-fusion network for semi-automatic segmentation of skin lesions;Bi;Med. Image Anal.,2022

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