Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI

Liu, Mingxia; Wang, Li; Shen, Dinggang

doi:10.1109/tnnls.2021.3055772

Cited by 31 publications

(13 citation statements)

References 46 publications

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“…Therefore, the background portions in these image patches are also with redder color. This coarse visualization is a common phenomenon in current methods, such as in ( 36) and (37). However, this phenomenon does not affect the performance of THAN.…”

Section: Evaluation Of the Ismentioning

confidence: 90%

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

Zhang

Gao

Jin

et al. 2021

Quant Imaging Med Surg

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Background: To assist doctors to diagnose mild cognitive impairment (MCI) and Alzheimer's disease (AD) early and accurately, convolutional neural networks based on structural magnetic resonance imaging (sMRI) images have been developed and shown excellent performance. However, they are still limited in their capacity in extracting discriminative features because of large sMRI image volumes yet small lesion regions and the small number of sMRI images.Methods: We proposed a task-driven hierarchical attention network (THAN) taking advantage of the merits of patch-based and attention-based convolutional neural networks for MCI and AD diagnosis. THAN consists of an information sub-network and a hierarchical attention sub-network. In the information subnetwork, an information map extractor, a patch-assistant module, and a mutual-boosting loss function are designed to generate a task-driven information map, which automatically highlights disease-related regions and their importance for final classification. In the hierarchical attention sub-network, a visual attention module and a semantic attention module are devised based on the information map to extract discriminative features for disease diagnosis.Results: Extensive experiments were conducted for four classification tasks: MCI versus (vs.) normal controls (NC), AD vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. Results demonstrated that THAN attained the accuracy of 81.6% for MCI vs. NC, 93.5% for AD vs. NC, 80.8% for AD vs. MCI, and 62.9% for AD vs. MCI vs. NC. It outperformed advanced attention-based and patch-based methods. Moreover, information maps generated by the information sub-network could highlight the potential biomarkers of MCI and AD, such as the hippocampus and ventricles. Furthermore, when the visual and semantic attention modules were combined, the performance of the four tasks was highly improved. Conclusions:The information sub-network can automatically highlight the disease-related regions.The hierarchical attention sub-network can extract discriminative visual and semantic features. Through the two sub-networks, THAN fully exploits the visual and semantic features of disease-related regions and meanwhile considers global features of sMRI images, which finally facilitate the diagnosis of MCI and AD.

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Section: Evaluation Of the Ismentioning

confidence: 90%

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

Zhang

Gao

Jin

et al. 2021

Quant Imaging Med Surg

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“…More recently, artificial intelligence techniques represented by machine/deep learning have been extensively applied to a growing number of studies to assist or partly replace clinicians in decision making ( 10 – 12 ). As an extension of our previous work adopting traditional statistical methods (line regression and Logistic regression) when modeling, we in this study employed the more advanced machine-learning methods to tease out the optimal algorithm and deep-learning models to validate the contribution of the thrifty panel of important LRTI-susceptibility factors selected by the machine-learning methods.…”

Section: Discussionmentioning

confidence: 99%

“…The reasons for this challenge are partly due to the difficulty in delineating more complicated and nuanced relationship among factors to predict LRTI when adopting traditional statistical methods (such as Logistic regression analysis), which involve only one input-output layer and accommodate relatively small amounts of variation. To overcome this challenge, more advanced machine-learning methods have been developed and successfully applied in a variety of clinical settings ( 10 – 12 ). To our knowledge, there is to date no application of machine-learning methods in the field of LRTI.…”

Section: Introductionmentioning

confidence: 99%

Factors Associated With Lower Respiratory Tract Infection Among Chinese Students Aged 6–14 Years

et al. 2022

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AimsWe employed machine-learning methods to explore data from a large survey on students, with the goal of identifying and validating a thrifty panel of important factors associated with lower respiratory tract infection (LRTI).MethodsCross-sectional cluster sampling was performed for a survey of students aged 6–14 years who attended primary or junior high school in Beijing within January, 2022. Data were collected via electronic questionnaires. Statistical analyses were completed using the PyCharm (Edition 2018.1 x64) and Python (Version 3.7.6).ResultsData from 11,308 students (5,527 girls and 5,781 boys) were analyzed, and 909 of them had LRTI with the prevalence of 8.01%. After a comprehensive evaluation, the Gaussian naive Bayes (gNB) algorithm outperformed the other machine-learning algorithms. The gNB algorithm had accuracy of 0.856, precision of 0.140, recall of 0.165, F1 score of 0.151, and area under the receiver operating characteristic curve (AUROC) of 0.652. Using the optimal gNB algorithm, top five important factors, including age, rhinitis, sitting time, dental caries, and food or drug allergy, had decent prediction performance. In addition, the top five factors had prediction performance comparable to all factors modeled. For example, under the sequential deep-learning model, the accuracy and loss were separately gauged at 92.26 and 25.62% when incorporating the top five factors, and 92.22 and 25.52% when incorporating all factors.ConclusionsOur findings showed the top five important factors modeled by gNB algorithm can sufficiently represent all involved factors in predicting LRTI risk among Chinese students aged 6–14 years.

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“…Each vector in I k can be regarded as a class-specific response after extracting the multiple slices-level features using the MVSSN. Considering that the volumetric MRI data contains different slices, many of them may not contain the most representative information relevant to dementia (Lian et al, 2021 ). To address this issue, we proposed a SAM to help the CNN focus on the specific features by exploiting the interdependencies among slices.…”

Section: Methodsmentioning

confidence: 99%

Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network

Chen

Qiao

Zhu

2022

Front. Aging Neurosci.

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Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN). Specifically, we begin by extracting the local slice-level characteristic in various dimensions using multiple sub-networks. Then we proposed a slice-level attention mechanism to emphasize specific 2D-slices to exclude the redundancy features. After that, a 3D-CNN was employed to capture the global subject-level structural changes. Finally, all these 2D and 3D features were fused to obtain more discriminative representations. We conduct the experiments on 1,451 subjects from ADNI-1 and ADNI-2 datasets. Experimental results showed the superiority of our model over the state-of-the-art approaches regarding dementia classification. Specifically, our model achieves accuracy values of 91.1 and 80.1% on ADNI-1 for AD diagnosis and mild cognitive impairment (MCI) convention prediction, respectively.

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Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI

Cited by 31 publications

References 46 publications

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease

Factors Associated With Lower Respiratory Tract Infection Among Chinese Students Aged 6–14 Years

Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network

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