Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization (2018) Breast cancer. WHO website
Homer MJ (1997) Mammographic interpretation: a practical approach, 2nd edn. McGraw-Hill, New York, p 376 Health Professions Division
Rangayyan RM, Ayres FJ, Leo Desautels JE (2007) A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Franklin Inst 344(3–4):312–348
Oelze ML (2012) Quantitative ultrasound techniques and improvements to diagnostic ultrasonic imaging. In: IEEE international ultrasonics symposium, IUS
Tadayyon H, Sadeghi-Naini A, Wirtzfeld L, Wright FC, Czarnota G (2014) Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties. Med Phys 41(1):012903
Ruiz H, Ortega-Martorell S, Jarman IH, MartÃn JD, Lisboa PJG (2012) Constructing similarity networks using the Fisher information metric. In: European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), Bruges, Belgium, pp 191–196
Ruiz H, Jarman IH, MartÃn JD, Lisboa PJG (2011) The role of Fisher information in primary data space for neighbourhood mapping. In: European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), Bruges, Belgium, pp 381–386
Suckling J, Parker J, Dance D (1994) The mammographic image analysis society digital mammogram database. In: Exerpta medica international congress series
Zhao D, Shridhar M, Daut DG (1992) Morphology on detection of calcifications in mammograms. In: Proceedings of the 1992 IEEE international conference on acoustics, speech, and signal processing, ICASSP-92, vol 3. IEEE, pp 129–132
Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 3(1):94–100
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621
Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 5(1):25–39
Laine A, Fan J (1993) Texture classification by wavelet packet signatures. IEEE Trans Pattern Anal Mach Intell 15(11):1186–1191
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Routledge, Abingdon
Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B 38:321–330
Young G, Householder AS (1938) Discussion of a set of points in terms of their mutual distances. Psychometrika 3(1):19–22
Uppal MTN Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features. Biomed Res 27(2):322–327
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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. In: Vellido, A., Gibert, K., Angulo, C., MartÃn Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-19642-4_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19641-7
Online ISBN: 978-3-030-19642-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)