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Items where Author is "Riley, P"

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Number of items: 7.

Article

Ortega Martorell, S, Riley, P, Olier-Caparroso, I, Raidou, RG, CasaƱa-Eslava, RV, Rea, M, Shen, L, Lisboa, PJG and Palmieri, C (2022) Breast cancer patient characterisation and visualisation using deep learning and fisher information networks. Scientific Reports, 12 (1). p. 14004. ISSN 2045-2322

Ortega Martorell, S, Candiota, AP, Thomson, R, Riley, P, Julia-Sape, M and Olier, I (2019) Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models. PLoS One, 14 (8). pp. 1-21. ISSN 1932-6203

Kasper, AM, Crighton, B, Langan-Evans, C, Riley, P, Sharma, A, Close, GL and Morton, JP (2018) Case Study: Extreme Weight Making Causes Relative Energy Deficiency, Dehydration and Acute Kidney Injury in a Male Mixed Martial Arts Athlete. International Journal of Sport Nutrition and Exercise Metabolism. ISSN 1526-484X

Conference or Workshop Item

Srivastava, M, Olier, I, Riley, P, Lisboa, P and Ortega-Martorell, S (2019) Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer. In: Advances in Intelligent Systems and Computing , 976. pp. 304-313. (13th International Workshop, WSOM+ 2019, 26th-28th June 2019, Barcelona, Spain).

Riley, P, Olier, I, Rea, M, Lisboa, P and Ortega-Martorell, S (2019) A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI. In: Advances in Intelligent Systems and Computing , 976. (13th International Workshop, WSOM+ 2019, 26th-28th June 2019, Barcelona, Spain).

Marnell, S, Riley, P, Olier, I, Rea, M and Ortega-Martorell, S A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions. In: Lecture Notes in Computer Science . (20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), 14 November 2019 - 16 November 2019, Manchester). (Accepted)

Thesis

Riley, P (2022) Explainable machine learning models to assist with cancer diagnosis. Doctoral thesis, Liverpool John Moores University.

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