Landmark-based deep multi-instance learning for brain disease diagnosis

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

doi:10.1016/j.media.2017.10.005

Cited by 339 publications

(206 citation statements)

References 54 publications

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“…To address this issue, for all identified landmarks ranked according to p -values in descending manner, we further define a spatial Euclidean distance threshold (i.e., 16) to control the distance between landmarks, to reduce the overlaps among image patches. Finally, we select the top 50 landmarks for deep feature learning [34], and show these landmarks in Fig. 3(b), Fig.…”

Section: Methodsmentioning

confidence: 99%

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Liu

Zhang

Nie

et al. 2018

IEEE J. Biomed. Health Inform.

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123

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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 paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer’s disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer’s disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer’s disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.

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Section: Methodsmentioning

confidence: 99%

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Liu

Zhang

Nie

et al. 2018

IEEE J. Biomed. Health Inform.

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123

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“…Alzheimer's disease [275] Landmark-based deep multi-instance learning evaluated on 1526 subjects from three public datasets (ADNI-1, ADNI-2, MIRIAD) [276] Identify different stages of AD [296] Addressed the challenge of automated pathogenesis-based diagnosis, simultaneously localizing and grading multiple spinal structures (neural foramina, vertebrae, intervertebral discs) for diagnosing LNFS and discover pathogenic factors. Proposed a deep multiscale multitask learning network integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network where (i) a DMML-Net merges semantic representations to reinforce the salience of numerous target organs (ii) a DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs, and (iii) a DMML-Net joins the multitask regression module and the multitask loss module to combine the mutual benefit between tasks…”

Section: Diagnosis and Predictionmentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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“…Results on the ABIDE database demonstrate the effectiveness of our method in ASD diagnosis using rs-fMRI data acquired from multiple centers. In the future, we will perform data-driven feature extraction for rs-fMRI data via deep learning [9–11] rather than using current hand-crafted ( i.e ., ROI) features, which is expected to further improve the diagnostic performance.…”

Section: Resultsmentioning

confidence: 99%

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification

Wang

Zhang

Huang

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large number of subjects from multiple centers are beneficial for investigating the pathological changes of ASD. To better utilize the multi-center data, various machine learning methods have been proposed. However, most previous studies do not consider the problem of data heterogeneity (e.g., caused by different scanning parameters and subject populations) among multi-center datasets, which may degrade the diagnosis performance based on multi-center data. To address this issue, we propose a multi-center low-rank representation learning (MCLRR) method for ASD diagnosis, to seek a good representation of subjects from different centers. Specifically, we first choose one center as the target domain and the remaining centers as source domains. We then learn a domain-specific projection for each source domain to transform them into an intermediate representation space. To further suppress the heterogeneity among multiple centers, we disassemble the learned projection matrices into a shared part and a sparse unique part. With the shared matrix, we can project target domain to the common latent space, and linearly represent the source domain datasets using data in the transformed target domain. Based on the learned low-rank representation, we employ the k-nearest neighbor (KNN) algorithm to perform disease classification. Our method has been evaluated on the ABIDE database, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods.

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Landmark-based deep multi-instance learning for brain disease diagnosis

Cited by 339 publications

References 54 publications

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

An overview of deep learning in medical imaging focusing on MRI

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification

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