A support vector machine approach for detection of microcalcifications

By Issam El-Naqa, Yongyi Yang, Miles N. Wernick, N. Galatsanos, Robert M. Nishikawa. IEEE Transactions on Medical Imaging, 2002

574 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f1d73520d51a0f19c686d9f4bf7af1db33763a2a

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Citations in the corpus

The role of quantitative PET in predicting cancer treatment outcomes (2014, methodology): “These methods have been applied successfully in many diverse areas including outcome prediction [40, 83–86].”
Classification of hyperspectral remote sensing images with support vector machines (2004, background): “The growing interest in SVMs [27]–[30] is confirmed by their successful implementation in numerous other pattern recognition applications such as biomedical imaging [31], image compression [32], and three-dimensional object recognition [33].”
Consensus-Based Distributed Support Vector Machines (2010, background): “…classification tasks emerging inappl cations such as medical imaging, bio-informatics, speech, and handwriting recognition, to name a few (Vapnik, 1998; Scḧolkopf and Smola, 2002; El-Naqa et al., 2002; Liang et al., 2007; Ganapathiraju et al., 2004; Li, 2005; Markowska-Kaczmar and Kubacki, 2005).”
Machine Learning and Radiology (2012, methodology): “first showed that it is feasible to detect microcalcifications from digital mammograms using support vector machines (El-Naqa et al., 2002).”
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes (2009, background): “Therefore, they have been widely applied in many complex industrial and medical pattern recognition tasks [20-25].”
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, methodology): “SVM framework has been presented for automated detection of microcalcification in mammogram [55].”
A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, methodology): “[60] 76 mammograms containing 1, 120 microcalcifications Sensitivity of 94% at one false positive per image Wei et al.”
A similarity learning approach to content-based image retrieval: application to digital mammography (2004, methodology): “In our own work [ 28 ], we developed an SVM-based approach for detection of microcalcifications in mammograms, and demonstrated using clinical mammogram data that such an approach could outperform several well-known methods in the literature.”
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (2006, background): “D) object recognition [18], biomedical imaging [19], and image compression [20].”
Machine learning for detection and diagnosis of disease. (2006, background): “SVMs have been applied to a range of biomedical disease detection and diagnosis problems, including detection of oral cancers in optical images (70), polyps in CT colonography (71), and detection of microcalcifications in mammograms (72).”
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, background): “D object recognition [12], biomedical imaging [13], image compression [14], and remote sensing [15], [16].”
Breast cancer detection using deep convolutional neural networks and support vector machines (2019, background): “Thus, the goal of the SVM is to find the optimum hyper-plane that separates clusters of target vectors on the opposing sides of the plane (El-naqa et al., 2002).”
Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (2015, methodology): “More advanced methods, such as ISOMAP [28], kernel SVM [29], [30], locally linear embedding [31], [32], and sparse coding [33], can learn the nonlinear embedding directly from the observed data within a single layer of projection.”