End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network

Computer-aided prediction of dementia status (e.g., clinical scores of cognitive tests) from brain MRI is of great clinical value, as it can help assess pathological stage and predict disease progression. Existing learning-based approaches typically preselect dementia-sensitive regions from the whole-brain MRI for feature extraction and prediction model construction, which might be sub-optimal due to potential heterogeneities between different steps. Also, based on anatomical prior knowledge (e.g., brain atlas) and time-consuming nonlinear registration, these preselected brain regions are usually the same across all subjects, ignoring their individual specificities in dementia progression. In this paper, we propose a multi-task weakly-supervised attention network (MWAN) to jointly predict multiple clinical scores from the baseline MRI data, by explicitly considering individual specificities of different subjects. Leveraging a fully-trainable dementia attention block, our MWAN method can automatically identify subject-specific discriminative locations from the whole-brain MRI for end-to-end feature learning and multi-task regression. We evaluated our MWAN method by cross-validation on two public datasets (i.e., ADNI-1 and ADNI-2). Experimental results demonstrate that the proposed method performs well in both the tasks of clinical score prediction and weakly-supervised discriminative localization in brain MR images.

[1]  Dinggang Shen,et al.  Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores , 2020, IEEE Transactions on Cybernetics.

[2]  Christos Davatzikos,et al.  A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages , 2017, NeuroImage.

[3]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Mert R. Sabuncu,et al.  Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study , 2014, Neuroinformatics.

[5]  J. Baron,et al.  In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer's Disease , 2001, NeuroImage.

[6]  Dinggang Shen,et al.  Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis , 2019, IEEE Transactions on Biomedical Engineering.

[7]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[8]  Dinggang Shen,et al.  Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Ming-Hsuan Yang,et al.  Weakly Supervised Coupled Networks for Visual Sentiment Analysis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[10]  William Jagust,et al.  Vulnerable Neural Systems and the Borderland of Brain Aging and Neurodegeneration , 2013, Neuron.

[11]  Sid E O'Bryant,et al.  Staging dementia using Clinical Dementia Rating Scale Sum of Boxes scores: a Texas Alzheimer's research consortium study. , 2008, Archives of neurology.

[12]  Yaozong Gao,et al.  Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis , 2016, IEEE Transactions on Medical Imaging.

[13]  Jiayu Zhou,et al.  Modeling disease progression via multi-task learning , 2013, NeuroImage.