ArticleIEEE transactions on cybernetics2020
Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.
Article in IEEE transactions on cybernetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Weakly Supervised MRI-Based Classification of Alzheimer's Disease Using Clinical Pseudo-Labels.NeuroSci · 2026Article
- Alzheimer's disease prediction algorithm based on hippocampal longitudinal hybrid morphological features.Quantitative imaging in medicine and surgery · 2026Article
- Learning discrete structures for cancer radiomics.APL bioengineering · 2025Article
- An imaging genetics network model for clinical score assessment in Alzheimer's disease.PNAS nexus · 2025Article
- Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach.Scientific reports · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.Journal of medical systems · 2023Article
- Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors.iScience · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism.Brain sciences · 2022Article
- Early detection of Alzheimer's disease using neuropsychological tests: a predict-diagnose approach using neural networks.Brain informatics · 2022Article
- Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.IEEE transactions on cybernetics · 2022Article
- Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey.Archives of computational methods in engineering : state of the art reviews · 2022Article
- Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network.Frontiers in aging neuroscience · 2022Article
- Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.Quantitative imaging in medicine and surgery · 2022Article
- Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.Scientific reports · 2021Article
- Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.Brain informatics · 2021Article
- Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images.IEEE transactions on medical imaging · 2021Article
- Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification.Medical image analysis · 2021Article
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4 authors.
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Abstract
As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and predict future development of the disease. Due to incomplete clinical labels/scores, previous learning-based studies often simply discard subjects without ground-truth scores. This would result in limited training data for learning reliable and robust models. Also, existing methods focus only on using hand-crafted features (e.g., image intensity or tissue volume) of MRI data, and these features may not be well coordinated with prediction models. In this paper, we propose a weakly supervised densely connected neural network (wiseDNN) for brain disease prognosis using baseline MRI data and incomplete clinical scores. Specifically, we first extract multiscale image patches (located by anatomical landmarks) from MRI to capture local-to-global structural information of images, and then develop a weakly supervised densely connected network for task-oriented extraction of imaging features and joint prediction of multiple clinical measures. A weighted loss function is further employed to make full use of all available subjects (even those without ground-truth scores at certain time-points) for network training. The experimental results on 1469 subjects from both ADNI-1 and ADNI-2 datasets demonstrate that our proposed method can efficiently predict future clinical measures of subjects.
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