2018
DOI: 10.1109/jbhi.2018.2791863
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Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Abstract: Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this pa… Show more

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Cited by 123 publications
(53 citation statements)
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References 48 publications
(61 reference statements)
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“…Liu et al, focused on another difficult task in using MRI for diagnosis of brain diseases including Alzheimer's disease -that of automatic generation of clinically meaningful features. They proposed a novel approach of identifying discriminative regions called landmarks, followed by application of a CNN for patch-based deep feature learning [10]. Addressing challenges in other common imaging modalities, Gruezemacher et al, developed a three-dimensional (3D) adaptation of deep neural nets (DNNs) for lung nodule detection from computed tomography (CT) images [6]; Hassan et al, presented a novel approach combining tensor-based segmentation, Delauday triangulation, and deep learning models to provide complete 3D presentation of macula to support automated diagnosis of various macula conditions [7].…”
Section: Discussionmentioning
confidence: 99%
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“…Liu et al, focused on another difficult task in using MRI for diagnosis of brain diseases including Alzheimer's disease -that of automatic generation of clinically meaningful features. They proposed a novel approach of identifying discriminative regions called landmarks, followed by application of a CNN for patch-based deep feature learning [10]. Addressing challenges in other common imaging modalities, Gruezemacher et al, developed a three-dimensional (3D) adaptation of deep neural nets (DNNs) for lung nodule detection from computed tomography (CT) images [6]; Hassan et al, presented a novel approach combining tensor-based segmentation, Delauday triangulation, and deep learning models to provide complete 3D presentation of macula to support automated diagnosis of various macula conditions [7].…”
Section: Discussionmentioning
confidence: 99%
“…Section editors screened this initial list for relevance to the theme and scientific quality, and they rated each paper as "keep," "pend," or "discard." Papers rated as "keep" by one of the section editors were independently reviewed and scored by section editors to yield the top 15 candidate best papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Criteria for scoring included innovation beyond established AI techniques, work that addressed substantial challenges in the field, and rigorous scientific evaluations.…”
Section: Paper Selection Methodsmentioning
confidence: 99%
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“…Three public datasets are applied in this study including the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), ADNI-2, and the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset [38]. The subjects include 194 AD and 226 normal controls (NC) with 1.5T T1weighted structure MRI data in the baseline ADNI-1 dataset.…”
Section: A Studied Datasetsmentioning
confidence: 99%
“…The development of machine learning (ML) approaches for computer-aided diagnosis and prognosis of AD has been a very active topic in the past decade, e.g. 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 . In particular, numerous ML methods 14,15,21,28,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47 have been proposed to predict progression to AD among patients with MCI from neuroimaging data (see e.g 48,49,50 for reviews on that topic).…”
Section: Introductionmentioning
confidence: 99%