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<https://discovery.ucl.ac.uk/id/eprint/16359> <http://purl.org/dc/terms/title> "Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/16359> <http://purl.org/ontology/bibo/abstract> "Elastic scattering spectroscopy (ESS), is a non-invasive and real-time in vivo\r\noptical diagnosis technique sensitive to changes in the physical properties of human\r\ntissue, and thus able to detect early cancer and precancerous changes. This thesis\r\nfocuses on the statistical issue on how to eliminate irrelevant variations in the high-dimensional\r\nESS spectra and extract the most useful information to enable the\r\nclassification of tissue as normal or abnormal.\r\nMultivariate statistical methods have been used to tackle the problems, among\r\nwhich principal component discriminant analysis and partial least squares\r\ndiscriminant analysis are the most explored throughout the thesis as general tools for\r\nsupervised dimension reduction and classification. Customized multivariate methods\r\nare proposed in the specific context of ESS.\r\nWhen ESS spectra are measured in vivo by a hand-held optical probe, differences\r\nin the angle and pressure of the probe are a major source of variability between the\r\nspectra from replicate measurements. A customized spectral pre-treatment called error\r\nremoval by orthogonal subtraction (EROS) is designed to ameliorate the effect of this\r\nvariability. This pre-treatment reduces the complexity and increases both the accuracy\r\nand interpretability of the subsequent classification models when applied to early\r\ndetection of cancer risk in Barrett’s oesophagus.\r\nFor the application of ESS to diagnosis of sentinel lymph node metastases in\r\nbreast cancer, an automated ESS scanner was developed to take measurements from a\r\nlarger area of tissue to produce ESS images for cancer diagnosis. Problems arise due\r\nto the existence of background area in the image with considerable between-node\r\nvariation and no training data available. A partially supervised Bayesian multivariate\r\nfinite mixture classification model with a Markov random field spatial prior in a\r\nreduced dimensional space is proposed to recognise the background area\r\nautomatically at the same time as distinguishing normal from metastatic tissue."^^<http://www.w3.org/2001/XMLSchema#string> .
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