Fast support vector machine training and classification on graphics processors

Catanzaro, Bryan; Sundaram, Narayanan; Keutzer, Kurt

doi:10.1145/1390156.1390170

Cited by 301 publications

(171 citation statements)

References 16 publications

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“…Thankfully, the SVM was found to generalize well based on a limited data set. Moreover, by performing part of the computation on a graphics processing unit (GPU), it has been shown possible to reduce computation time by a factor of around 120-150 [17].…”

Section: Discussionmentioning

confidence: 99%

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Sjölund

Järlideni

Andersson

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Magnetic resonance (MR) images lack information about radiation transport-a fact which is problematic in applications such as radiotherapy planning and attenuation correction in combined PET/MR imaging. To remedy this, a crude but common approach is to approximate all tissue properties as equivalent to those of water. We improve upon this using an algorithm that automatically identifies bone tissue in MR. More specifically; we focus on segmenting the skull prior to stereotactic neurosurgery, where it is common that only MR images are available.In the proposed approach, a machine learning algorithm known as a support vector machine is trained on patients for which both a CT and an MR scan are available. As input, a combination of local and global information is used. The latter is needed to distinguish between bone and air as this is not possible based only on the local image intensity.A whole skull segmentation is achievable in minutes. In a comparison with two other methods, one based on mathematical morphology and the other on deformable registration, the proposed method was found to yield consistently better segmentations.

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Section: Discussionmentioning

confidence: 99%

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Sjölund

Järlideni

Andersson

et al. 2014

2014 22nd International Conference on Pattern Recognition

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“…In recognition, GPU based feature detectors and trackers [15,16] have been proposed, as have learning components such as support vector machines [17] and k-nearest neighbors [18]. Recently, Wojek et al .…”

Section: Related Workmentioning

confidence: 99%

Really Quick Shift: Image Segmentation on a GPU

Fulkerson

Soatto

2012

Trends and Topics in Computer Vision

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“…5b). The first and second order features of these RFs were classified by the trained GPU-based SVM [37] achieving an average 17-fold speed up for the detection of invasive areas over the WSIs in comparison with a standard implementation. However, since only few regions were selected for classification in order to reduce computation time, an interpolation with a low-pass Gaussian filtering is applied to the SVM classification results (see Fig.…”

Section: Training and Testingmentioning

confidence: 99%

Time-efficient sparse analysis of histopathological whole slide images

Huang

Veillard

Roux

et al. 2011

Computerized Medical Imaging and Graphics

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Histopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.

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Fast support vector machine training and classification on graphics processors

Cited by 301 publications

References 16 publications

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Really Quick Shift: Image Segmentation on a GPU

Time-efficient sparse analysis of histopathological whole slide images

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