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Claim this profileWorks127 from public data
- 1,373
- Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment390
A deep learning approach based on convolutional neural networks, designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data, outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity.
- Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network291
A convolutional neural network to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD is proposed and yields a state-of-the-art performance.
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- Multiple instance learning for classification of dementia in brain MRI214
The proposed multiple instance learning (MIL) method for Alzheimer's disease and its prodromal stage mild cognitive impairment can achieve similar or improved results, providing an alternative framework for the detection and prediction of neurodegenerative diseases.
- Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling209
A novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Discriminative dictionary learning for abdominal multi-organ segmentation154
An automated segmentation method is presented for multi-organ segmentation in abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
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- Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer’s Disease124
The experimental results show that the structural and functional information of brain tissue can be mapped well and that the image synthesized by the proposed method is close to the real image, demonstrating the effectiveness of the proposed framework.
- Image Super-Resolution Using Knowledge Distillation95
The proposed method significantly improves the SR performance of a student network without changing its structure, which means that the computational time and the memory consumption do not increase during the testing stage while theSR performance is significantly improved.
- Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data92
The results showed that the proposed framework for AD multiclass diagnosis with a linear discriminant analysis (LDA) scoring method achieved a better performance than the method that did not score multimodal data and the methods in previous studies, thereby indicating that the LDA scoring strategy is an efficient way for multimodalities fusion inAD multiclass classification.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data78
An extreme learning machine (ELM)-based grading method is developed to efficiently fuse multimodal data and predict MCI-to-AD conversion, resulting in an accurate prediction of MCI-to-AD conversion.
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- CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation45
The proposed CoTrFuse network is a network that combines the strengths of both CNN and Transformer that outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation.
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- Graph Reasoning-Based Emotion Recognition Network41
This paper proposes a novel framework based on the graph convolutional network for emotion recognition by utilizing the semantic relationships of different regions to achieve superior performance over state-of-the-art approaches for the emotion recognition on the CEAR and AFEW datasets.
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- A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation36
A novel framework to improve the accuracy of thyroid nodules segmentation and provide a new way of ultrasound image segmentation that outperforms most state-of-the-art methods and can provide doctors with reliable auxiliary diagnosis information in clinical practice.
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- Supervoxel Classification Forests for Estimating Pairwise Image Correspondences32
A general method for establishing pairwise correspondences, which is a fundamental problem in image analysis, and the quality of the correspondences is determined in a label propagation setting, demonstrating the potential of supervoxel classification forests for estimating image correspondences.
- N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation31
A novel N-shape dense fully convolutional neural network for medical image segmentation, referred to as N-Net, which outperforms several state-of-the-art methods in the thyroid nodule segmentation tasks.
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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