Patrick Riley
School of Computer Science and Mathematics
Faculty of Engineering and Technology
Email: P.J.Riley@2014.ljmu.ac.uk
PhD Researcher in the Department of Applied Mathematics. Part of the Machine Learning Research Group; Supervisor is Sandra Ortega-Martorell.
Funded by the LJMU Scholarship fund.
Degrees
Liverpool John Moores University, UK, Data Science MSc
Liverpool John Moores University, United Kingdom, Mathematics BSc
Journal article
Ortega Martorell S, Riley P, Olier-Caparroso I, Raidou RG, Casaña-Eslava RV, Rea M, Shen L, Lisboa PJG, Palmieri C. 2022. Breast cancer patient characterisation and visualisation using deep learning and fisher information networks Scientific Reports, 12 :14004 DOI Author Url Publisher Url Public Url
Ortega Martorell S, Candiota AP, Thomson R, Riley P, Julia-Sape M, Olier I. 2019. Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models Gozzi A. PLoS One, 14 :1-21 DOI Author Url Publisher Url Public Url
Thesis/Dissertation
Riley P. 2022. Explainable machine learning models to assist with cancer diagnosis Ortega Martorell S, Olier I, Lisboa P. Public Url
Conference publication
Riley P, Olier I, Rea M, Lisboa P, Ortega-Martorell S. 2020. A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI Advances in Intelligent Systems and Computing, 13th International Workshop, WSOM+ 2019 976 :294-303 DOI Publisher Url Public Url
Srivastava M, Olier I, Riley P, Lisboa P, Ortega-Martorell S. 2020. Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer Advances in Intelligent Systems and Computing, 13th International Workshop, WSOM+ 2019 976 :304-313 DOI Publisher Url Public Url
Marnell S, Riley P, Olier I, Rea M, Ortega-Martorell S. A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions Lecture Notes in Computer Science, 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) DOI Author Url Publisher Url Public Url
Conference presentation:
A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions, International Conference on Intelligent Data Engineering and Automated Learning, Manchester, UK, Oral presentation. 2019
A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI, WSOM+ 2019, UPC, Barcelona, Spain, Oral presentation. 2019