The Experts below are selected from a list of 92130 Experts worldwide ranked by ideXlab platform
Gongping Yang - One of the best experts on this subject based on the ideXlab platform.
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hierarchical retinal blood vessel segmentation based on feature and ensemble learning
Neurocomputing, 2015Co-Authors: Shuangling Wang, Yilong Yin, Guibao Cao, Benzheng Wei, Yuanjie Zheng, Gongping YangAbstract:Segmentation of retinal blood vessels is of substantial clinical importance for diagnoses of many diseases, such as diabetic retinopathy, hypertension and cardiovascular diseases. In this paper, the Supervised Method is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network (CNN) and Random Forest (RF). In this Method, CNN performs as a trainable hierarchical feature extractor and ensemble RFs work as a trainable classifier. By integrating the merits of feature learning and traditional classifier, the proposed Method is able to automatically learn features from the raw images and predict the patterns. Extensive experiments have been conducted on two public retinal images databases (DRIVE and STARE), and comparisons with other major studies on the same database demonstrate the promising performance and effectiveness of the proposed Method. A Supervised Method based on feature and ensemble learning is proposed.The whole pipeline of the proposed Method is automatic and trainable.Convolutional Neural Network performs as a trainable hierarchical feature extractor.Ensemble Random Forests work as a trainable classifier.Compared with state-of-the-arts, the experimental results are promising.
Sarah Barman - One of the best experts on this subject based on the ideXlab platform.
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an ensemble classification based approach applied to retinal blood vessel segmentation
IEEE Transactions on Biomedical Engineering, 2012Co-Authors: Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R Rudnicka, Christopher G Owen, Sarah BarmanAbstract:This paper presents a new Supervised Method for segmentation of blood vessels in retinal photographs. This Method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The Method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.
Joseph E Powell - One of the best experts on this subject based on the ideXlab platform.
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scpred accurate Supervised Method for cell type classification from single cell rna seq data
Genome Biology, 2019Co-Authors: Jose Alquicirahernandez, Anuja Sathe, Quan Nguyen, Joseph E PowellAbstract:Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable Method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction Method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized Method is available at https://github.com/powellgenomicslab/scPred/.
J M Bravo - One of the best experts on this subject based on the ideXlab platform.
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a new Supervised Method for blood vessel segmentation in retinal images by using gray level and moment invariants based features
IEEE Transactions on Medical Imaging, 2011Co-Authors: Diego Marin, Arturo Aquino, Manuel Emilio Gegundezarias, J M BravoAbstract:This paper presents a new Supervised Method for blood vessel detection in digital retinal images. This Method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The Method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The Method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
Jacques Hassoun - One of the best experts on this subject based on the ideXlab platform.
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protein expression profiling identifies subclasses of breast cancer and predicts prognosis
Cancer Research, 2005Co-Authors: Jocelyne Jacquemier, Christophe Ginestier, Jacques Rougemont, Valeriejeanne Bardou, Emmanuelle Charafejauffret, Jeannine Geneix, Jose Adelaide, Alane T Koki, Gilles Houvenaeghel, Jacques HassounAbstract:Breast cancer is a heterogeneous disease whose evolution is difficult to predict by using classic histoclinical prognostic factors. Prognostic classification can benefit from molecular analyses such as large-scale expression profiling. Using immunohistochemistry on tissue microarrays, we have monitored the expression of 26 selected proteins in more than 1,600 cancer samples from 552 consecutive patients with early breast cancer. Both an unSupervised approach and a new Supervised Method were used to analyze these profiles. Hierarchical clustering identified relevant clusters of coexpressed proteins and clusters of tumors. We delineated protein clusters associated with the estrogen receptor and with proliferation. Tumor clusters correlated with several histoclinical features of samples, including 5-year metastasis-free survival (MFS), and with the recently proposed pathophysiologic taxonomy of disease. The Supervised Method identified a set of 21 proteins whose combined expression significantly correlated to MFS in a learning set of 368 patients (P
