Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Automatic Detection of the Back Valley on Scoliotic Trunk Using Polygonal Surface Curvature
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Modelling of scoliotic deformities
International Journal of Modelling and Simulation
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Prediction of scoliosis curve type based on the analysis of trunk surface topography
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Principal spine shape deformation modes using riemannian geometry and articulated models
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Towards non invasive diagnosis of scoliosis using semi-supervised learning approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Hi-index | 0.00 |
This paper proposes a framework for the training of learning systems for regression when (i) the number of examples is small and contains interdependencies, and (ii) each sample consists of large quantities of discrete data that are functional in nature. The objective is to achieve robust yet nonlinear relations between inputs and outputs. In this study, laser scans of the trunk surface and reconstructions of spinal data from X-rays from scoliosis patients were functionally represented as surfaces and curves. Leading functional principal component coefficients thereof constituted comprehensive features, and achieved sufficient dimensionality reduction for the prediction of spine from trunk. As a learning method, support vector regression (SVR) was chosen for its strong generalizability capability that stems from penalizing model complexity. A first robust prediction in this research application was obtained, with coefficients of determination for leading outputs of 0.70 and 0.82, respectively, in the test set. Those translated to a spinal curve prediction L"2-error of 3.61mm, comparable to measurement error in data.