Keywords:
Multicentre study, Experimental, Retrospective, Tissue characterisation, Cancer, Experimental investigations, Diagnostic procedure, Image manipulation / Reconstruction, Digital radiography, Oncology, Breast, Artificial Intelligence, Artificial Intelligence and Machine Learning
Authors:
T. Doshi1, A. W. Preece2, D. Gibbins3, L. Tsui1; 1Bristol/UK, 2Bristol, Avon/UK, 3Bristol /UK
DOI:
10.26044/ecr2020/C-11330
Purpose
The Micrima MARIA® system is a Radio frequency (3-8GHz) imaging system [1] (Figure 1) that exploit dielectric contrast within breast tissue [2,3] for cancer detection.
Using a hemispherical conformal array of 60 antennas designed to fit round the shape of the breast and with antennas that look at the breast from all sides in a close packed pattern, 1770 independent channels are measured over 101 frequency points. These data are used to form a 3D focused image of scattered signals from within the breast. Raised dielectric contrast or additional vascularisation within lesions results in signal contrast, highlighting lesion position.
To aid diagnosis, classification of lesions as suspicious or benign is desirable. Since data are frequency dependent [4,5]. It is demonstrated that classification can be achieved by distinguishing between serum targets (mimicking high contrast fluid cysts or tumours) and propanediol (a low contrast target simulating a benign lesion) in an adipose phantom.
The aim of this work is to:
a) Present the comparative accuracy of three machine learning techniques for classification of frequency dependant radiowave data; and
b) Following the machine learning on a limited number of samples, to further compare the robustness of each method on unseen data.