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The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, non-invasively and quantitatively probes the biochemical and morphological changes that occur in pre-cancerous tissue. RBF ensemble algorithms based on such spectra provide automated, and near real-time implementation of pre-cancer detection in the hands of non-experts. The results are more reliable, direct and accurate than those achieved by either human experts or multivariate statistical algorithms.
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@inproceedings{tumer-ghosh_nips97, author="K. Tumer and N. Ramanujam and R. Richards-Kortum and J. Ghosh", title="Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks", booktitle= {Advances in Neural Information Processing Systems - 9}, editor = {Mozer, M. C. and Jordan, M. I. and Petsche, T.}, publisher = {M.I.T. Press}, pages={981-987}, abstract={The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, non-invasively and quantitatively probes the biochemical and morphological changes that occur in pre-cancerous tissue. RBF ensemble algorithms based on such spectra provide automated, and near real-time implementation of pre-cancer detection in the hands of non-experts. The results are more reliable, direct and accurate than those achieved by either human experts or multivariate statistical algorithms.}, bib2html_pubtype = {Refereed Conference Papers}, bib2html_rescat = {Classifier Ensembles}, year={1997} }
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