Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination

Zhou, Luping; Wang, Lei; Liu, Lingqiao; Ogunbona, Philip; Shen, Dinggang

doi:10.1007/978-3-319-02300-7_6

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…As a reference for the results obtained by the novel WeiRD approach, we chose to additionally perform support vector machine (SVM) classification, for two reasons. First, SVM is the most popular choice for neuroimaging classification problems . Second, SVM is still considered to perform best among many current classification techniques when computation time is considered and has been shown to robustly handle data with high dimensionality but few samples per class—two characteristics that are shared with the classification problems in the present study.…”

Section: Methodsmentioning

confidence: 96%

Decoding diagnosis and lifetime consumption in alcohol dependence from grey‐matter pattern information

Guggenmos

Scheel

Sekutowicz

et al. 2018

Acta Psychiatr Scand

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Section: Methodsmentioning

confidence: 96%

Decoding diagnosis and lifetime consumption in alcohol dependence from grey‐matter pattern information

Guggenmos

Scheel

Sekutowicz

et al. 2018

Acta Psychiatr Scand

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“…The feature vectors extracted are normally used to train a classification model, e.g. the support vector machine (SVM) [4], [11], [12], [17], [23], [25], [26] and sparse representation [10], [15], [16], [18], [20], [24], to obtain the image label.…”

Section: Introductionmentioning

confidence: 99%

Medical image classification with convolutional neural network

Cai

Wang

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

617

292

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Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

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“…We considered two different classifier types: support vector machine (SVM 61 ) and weighted robust distance (WeiRD 16,62,63 ). SVM is arguably the most popular classifier in the context of neuroimaging 64 , as it robustly handles data with high dimensionality but few samples per class. We used the implementation provided by libsvm 65 with a radial basis function kernel.…”

Section: Unimodal Diagnostic Classificationmentioning

confidence: 99%

A multimodal neuroimaging classifier for alcohol dependence

Guggenmos

Schmack

Veer

et al. 2020

Sci Rep

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With progress in magnetic resonance imaging technology and a broader dissemination of state-of-theart imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality-grey-matter density-by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modalityspecific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

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Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination

Cited by 8 publications

References 38 publications

Decoding diagnosis and lifetime consumption in alcohol dependence from grey‐matter pattern information

Decoding diagnosis and lifetime consumption in alcohol dependence from grey‐matter pattern information

Medical image classification with convolutional neural network

A multimodal neuroimaging classifier for alcohol dependence

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