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Literature DB >> 26430470

New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data.

Walker H Land¹, John J Heine², Tom Raway¹, Alda Mizaku¹, Nataliya Kovalchuk², Jack Y Yang³, Mary Qu Yang⁴.

Abstract

The automated decision paradigms presented in this work address the false positive (FP) biopsy occurrence in diagnostic mammography. An EP/ES stochastic hybrid and two kernelized Partial Least Squares (K-PLS) paradigms were investigated with following studies: methodology performance comparisonsautomated diagnostic accuracy assessments with two data sets. The findings showed: the new hybrid produced comparable results more rapidlythe new K-PLS paradigms train and operate Essentially in real time for the data sets studied. Both advancements are essential components for eventually achieving the FP reduction goal, while maintaining acceptable diagnostic sensitivities.

Entities: Disease

Keywords: computer aided diagnosis/classification; evolutionary programming/evolutionary strategies derived Support Vector Machines; kernel-partial least squares; machine intelligence

Year: 2008 PMID： 26430470 PMCID： PMC4587773 DOI： 10.1504/ijfipm.2008.020183

Source DB: PubMed Journal: Int J Funct Inform Personal Med ISSN： 1756-2104

17 in total

1. Use of border information in the classification of mammographic masses.

Authors: C Varela; S Timp; N Karssemeijer
Journal: Phys Med Biol Date: 2006-01-04 Impact factor: 3.609

2. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.

Authors: Michael E Mavroforakis; Harris V Georgiou; Nikos Dimitropoulos; Dionisis Cavouras; Sergios Theodoridis
Journal: Artif Intell Med Date: 2006-05-23 Impact factor: 5.326

3. Classification of breast masses in mammograms using genetic programming and feature selection.

Authors: R J Nandi; A K Nandi; R M Rangayyan; D Scutt
Journal: Med Biol Eng Comput Date: 2006-07-21 Impact factor: 2.602

4. Reliability analysis framework for computer-assisted medical decision systems.

Authors: Piotr A Habas; Jacek M Zurada; Adel S Elmaghraby; Georgia D Tourassi
Journal: Med Phys Date: 2007-02 Impact factor: 4.071

5. Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance.

Authors: Georgia D Tourassi; Brian Harrawood; Swatee Singh; Joseph Y Lo
Journal: Med Phys Date: 2007-08 Impact factor: 4.071

1. Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory.

Authors: Walker H Land; Dan Margolis; Ronald Gottlieb; Elizabeth A Krupinski; Jack Y Yang
Journal: BMC Genomics Date: 2010-12-01 Impact factor: 3.969

1 in total

New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data.

1. Use of border information in the classification of mammographic masses.

2. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.

3. Classification of breast masses in mammograms using genetic programming and feature selection.

4. Reliability analysis framework for computer-assisted medical decision systems.

5. Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance.

6. Linear and neural models for classifying breast masses.

7. Evolving artificial neural networks for screening features from mammograms.

8. Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions.

9. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.

10. Prediction of breast cancer malignancy using an artificial neural network.

1. Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory.