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).

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Abstract

© 2020, Springer Nature Switzerland AG. The aim of this paper is to build a computer based clinical decision support tool using a semi-supervised framework, the Fisher Information Network (FIN), for visualization of a set of mammographic images. The FIN organizes the images into a similarity network from which, for any new image, reference images that are closely related can be identified. This enables clinicians to review not just the reference images but also ancillary information e.g. about response to therapy. The Fisher information metric defines a Riemannian space where distances reflect similarity with respect to a given probability distribution. This metric is informed about generative properties of data, and hence assesses the importance of directions in space of parameters. It automatically performs feature relevance detection. This approach focusses on the interpretability of the model from the standpoint of the clinical user. Model predictions were validated using the prevalence of classes in each of the clusters identified by the FIN.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher:	Springer
Date Deposited:	24 Jul 2019 09:23
Last Modified:	13 Apr 2022 15:17
DOI or ID number:	10.1007/978-3-030-19642-4_30
URI:	https://researchonline.ljmu.ac.uk/id/eprint/11088

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