Image of Patrick Riley

Patrick Riley

School of Computer Science and Mathematics

Faculty of Engineering and Technology

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. 2019. 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) :132-138 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

Top