Breast Lesion Discrimination Using Saliency Features from MRI Sequences and MKL-Based Classification

Areiza-Laverde, Henry Jhoán; Duarte-Salazar, Carlos Andrés; Hernández, Liliana; Castro-Ospina, Andrés Eduardo; Díaz, Gabriel

doi:10.1007/978-3-030-33904-3_27

Cited by 1 publication

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“…A total of 10 perceptual features and 86 radiomic features were computed for each image sequence. Perceptual features were computed as the first five statistical moments from both the salient maps generated by the Graph-Based Visual Saliency method [ 56 , 57 ] and the original images; while radiomic features were extracted using the Pyradiomics toolbox of Python [ 58 ]. As described below, the experimental stage was performed by considering both each set of features as an independent information source and the combination of the two sets of features as one unique information source.…”

Section: Experimental Setupmentioning

confidence: 99%

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Areiza-Laverde

Castro-Ospina

Hernández

et al. 2020

Sensors

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Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.

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Section: Experimental Setupmentioning

confidence: 99%