Kagan Tumer's Publications

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Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks. K. Tumer, N. Ramanujam, R. Richards-Kortum, and J. Ghosh. In Advances in Neural Information Processing Systems - 9, pp. 981–987, M.I.T. Press, 1997.

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.

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BibTeX Entry

@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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