Partially Lazy Classification of Cardiovascular Risk via Multi-way Graph Cut Optimization

Cardiovascular disease (CVD) is considered a leading cause of human mortality with rising trends worldwide. Therefore, early identification of seemingly healthy subjects at risk is a priority. For this purpose, we propose a novel classification algorithm that provides a sound individual risk prediction, based on a non-invasive assessment of retinal vascular function. so-called lazy classification methods offer reduced time complexity by saving model construction time and better adapting to newly available instances, when compared to well-known eager methodS. Lazy methods are widely used due to their simplicity and competitive performance. However, traditional lazy approaches are more vulnerable to noise and outliers, due to their full reliance on the instances' local neighbourhood for classification. In this work, a learning method based on Graph Cut Optimization called GCO mine is proposed, which considers both the local arrangements and the global structure of the data, resulting in improved performance relative to traditional lazy methodS. We compare GCO mine coupled with genetic algorithms (hGCO mine) with established lazy and eager algorithms to predict cardiovascular risk based on Retinal Vessel Analysis (RVA) data. The highest accuracy of 99.52% is achieved by hGCO mine. The performance of GCO mine is additionally demonstrated on 12 benchmark medical datasets from the UCI repository. In 8 out of 12 datasets, GCO mine outperforms its counterpartS. GCO mine is recommended for studies where new instances are expected to be acquired over time, as it saves model creation time and allows for better generalization compared to state of the art methodS

Dynamic Ensemble Selection Approach for Hyperspectral Image Classification With Joint Spectral and Spatial Information

International audienceAccurate generation of a land cover map using hyperspectral data is an important application of remote sensing. Multiple classifier system (MCS) is an effective tool for hyperspec-tral image classification. However, most of the research in MCS addressed the problem of classifier combination, while the potential of selecting classifiers dynamically is least explored for hyper-spectral image classification. The goal of this paper is to assess the potential of dynamic classifier selection/dynamic ensemble selection (DCS/DES) for classification of hyperspectral images, which consists in selecting the best (subset of) optimal classifier(s) relative to each input pixel by exploiting the local information content of the image pixel. In order to have an accurate as well as com-putationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral–spatial classification model, which incorporates the spatial contextual information by using the Markov random field (MRF) with the proposed DES method. The proposed classification framework can be considered as a unified model to exploit the full spectral and spatial information. Classification experiments carried out on two different airborne hyperspectral images demonstrate that the proposed method yields a significant increase in the accuracy when compared to the state-of-the-art approaches

Imaging studies of peripheral nerve regeneration induced by porous collagen biomaterials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.There is urgent need to develop treatments for inducing regeneration in injured organs. Porous collagen-based scaffolds have been utilized clinically to induce regeneration in skin and peripheral nerves, however still there is no complete explanation about the underlying mechanism. This thesis utilizes advanced microscopy to study the expression of contractile cell phenotypes during wound healing, a phenotype believed to affect significantly the final outcome. The first part develops an efficient pipeline for processing challenging spectral fluorescence microscopy images. Images are segmented into regions of objects by refining the outcome of a pixel-wide model selection classifier by an efficient Markov Random Field model. The methods of this part are utilized by the following parts. The second part extends the image informatics methodology in studying signal transduction networks in cells interacting with 3D matrices. The methodology is applied in a pilot study of TGFP signal transduction by the SMAD pathway in fibroblasts seeded in porous collagen scaffolds. Preliminary analysis suggests that the differential effect of TGFP1 and TGFP3 to cells could be attributed to the "non-canonical" SMADI and SMAD5. The third part is an ex vivo imaging study of peripheral nerve regeneration, which focuses on the formation of a capsule of contractile cells around transected rat sciatic nerves grafted with collagen scaffolds, 1 or 2 weeks post-injury. It follows a recent study that highlights an inverse relationship between the quality of the newly formed nerve tissue and the size of the contractile cell capsule 9 weeks post-injury. Results suggest that "active" biomaterials result in significantly thinner capsule already 1 week post-injury. The fourth part describes a novel method for quantifying the surface chemistry of 3D matrices. The method is an in situ binding assay that utilizes fluorescently labeled recombinant proteins that emulate the receptor of , and is applied to quantify the density of ligands for integrins a113, a2p1 on the surface of porous collagen scaffolds. Results provide estimates for the density of ligands on "active" and "inactive" scaffolds and demonstrate that chemical crosslinking can affect the surface chemistry of biomaterials, therefore can affect the way cells sense and respond to the material.by Dimitrios S. Tzeranis.Ph. D