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Breast cancer patient characterisation and visualisation using deep learning and fisher information networks

Ortega Martorell, S, Riley, P, Olier-Caparroso, I, Raidou, RG, Casaña-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

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Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.

Item Type: Article
Uncontrolled Keywords: Breast; Breast Neoplasms; Deep Learning; Female; Humans; Information Services; Mammography
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Computer Science & Mathematics
Publisher: Nature Publishing Group
SWORD Depositor: A Symplectic
Date Deposited: 22 Aug 2022 09:50
Last Modified: 22 Aug 2022 10:00
DOI or ID number: 10.1038/s41598-022-17894-6
URI: https://researchonline.ljmu.ac.uk/id/eprint/17429
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