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- Multimodal classification of Alzheimer's disease and mild cognitive impairment1,315
This paper proposes to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance compared to the case of using an individual modality of biomarkers.
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- 3D conditional generative adversarial networks for high-quality PET image estimation at low dose449
Experimental results show that the proposed 3D c‐GANs method outperforms the benchmark methods and achieves much better performance than the state‐of‐the‐art methods in both qualitative and quantitative measures.
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- METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens232
The proposed METransformer, a method to realize a “multi-expert joint diagnosis” mechanism to upgrade the existing “single expert” framework commonly seen in the current literature, enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts.
- HEp-2 Cell Image Classification With Deep Convolutional Neural Networks226
The results on benchmark datasets demonstrate that the proposed framework can effectively outperform existing models by properly applying data augmentation, and the CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings.
- Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features210
The decreasing of network clustering coefficient of the olfactory cortex region was found in the AD patients, which suggests the olfactory dysfunction, and a significant decrease of the network clustering coefficient is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD.
- 3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis205
Experimental results show that the proposed 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) outperforms the traditional multi- modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.
- Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning204
This paper introduces two auxiliary tasks, i.e., a foreground and background reconstruction task for capturing semantic information and a signed distance field prediction task for imposing shape constraint, and proposes an uncertainty weighted integration (UWI) strategy for yielding the segmentation predictions of the teacher.
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- Cross-sectional and Longitudinal Analysis of the Relationship Between Aβ Deposition, Cortical Thickness, and Memory in Cognitively Unimpaired Individuals and in Alzheimer Disease187
The notion that Aβ deposition is not a benign process and that interventions with anti-Aβ therapy at these early stages have a higher chance to be effective is supported.
- A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking177
The strategies used to make Vision Transformer efficient are mathematically defined, described and discusses state-of-the-art methodologies, and their performances over different application scenarios are analyzed.
- A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification166
This paper highlights the presence of camera-level sub-domains as a unique characteristic in person Re-ID, and develops a “camera-aware” domain adaptation method via adversarial learning, and exploits the temporal continuity in each camera of target domain to create discriminative information.
- Augmentation Matters: A Simple-Yet-Effective Approach to Semi-Supervised Semantic Segmentation156
This work proposes AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance, and adopts a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space.
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- Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification129
This paper proposes a novel multiple kernel-learning framework to combine multimodal features for AD classification, which is scalable and easy to implement, and imposes the mixed L21 norm constraint on the kernel weights to enforce group sparsity among different feature modalities.
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- Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis113
An adaptive rectification based generative adversarial network with spectrum constraint, named AR-GAN, which uses low-dose PET (LPET) image to synthesize standard-dose SPET image of high-quality and outperform other state-of-the-art image synthesis approaches.
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- Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation100
A new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views and validate the new CCVC approach on the SSS benchmark datasets where the method achieves new state-of-the-art performance.
- Epileptic Seizure Forecasting With Generative Adversarial Networks100
This article proposes an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible, using the short-time Fourier transform on 28-s EEG windows as a pre-processing step and is the first application of GAN to seizure forecasting.
- Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices100
This paper proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages, which significantly elevates covariance representation to the unlimited opportunities provided by this new representation.
- ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease94
A novel multi-modality feature selection method, which performs feature selection and local similarity learning simultaniously, and a similarity matrix is learned by jointly considering different imaging modalities is proposed.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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