ArticleIEEE transactions on pattern analysis and machine intelligence2022
Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data.
Article in IEEE transactions on pattern analysis and machine intelligence, 2022. 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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24 citing papers in PubMed, 117 citations in OpenAlex.
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- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Evolution of deep learning for structural magnetic resonance imaging analysis in Alzheimer's disease: from slice-level classification to brain structure modeling.Frontiers in human neuroscience · 2026Review
- Distribution-Guided Multi-Tracer Brain PET Synthesis from Structural MRI with Class-Conditioned Weighted Diffusion.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Automatic Segmentation of Medial Temporal Lobe Subregions in Multi-Scanner, Multi-Modality Magnetic Resonance Imaging of Variable Quality.Hippocampus · 2025Article
- Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality magnetic resonance imaging of variable quality.bioRxiv : the preprint server for biology · 2025Article
- The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.International journal of legal medicine · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis.Scientific reports · 2024Article
- Addiction-related brain networks identification via Graph Diffusion Reconstruction Network.Brain informatics · 2024Article
- Generative AI unlocks PET insights: brain amyloid dynamics and quantification.Frontiers in aging neuroscience · 2024Article
- A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study.Quantitative imaging in medicine and surgery · 2023Article
- A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.EClinicalMedicine · 2023Article
- Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.European radiology · 2023Article
- Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Applications of generative adversarial networks in neuroimaging and clinical neuroscience.NeuroImage · 2023Review
- Generative AI for brain image computing and brain network computing: a review.Frontiers in neuroscience · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
Incomplete data problem is commonly existing in classification tasks with multi-source data, particularly the disease diagnosis with multi-modality neuroimages, to track which, some methods have been proposed to utilize all available subjects by imputing missing neuroimages. However, these methods usually treat image synthesis and disease diagnosis as two standalone tasks, thus ignoring the specificity conveyed in different modalities, i.e., different modalities may highlight different disease-relevant regions in the brain. To this end, we propose a disease-image-specific deep learning (DSDL) framework for joint neuroimage synthesis and disease diagnosis using incomplete multi-modality neuroimages. Specifically, with each whole-brain scan as input, we first design a Disease-image-Specific Network (DSNet) with a spatial cosine module to implicitly model the disease-image specificity. We then develop a Feature-consistency Generative Adversarial Network (FGAN) to impute missing neuroimages, where feature maps (generated by DSNet) of a synthetic image and its respective real image are encouraged to be consistent while preserving the disease-image-specific information. Since our FGAN is correlated with DSNet, missing neuroimages can be synthesized in a diagnosis-oriented manner. Experimental results on three datasets suggest that our method can not only generate reasonable neuroimages, but also achieve state-of-the-art performance in both tasks of Alzheimer's disease identification and mild cognitive impairment conversion prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.