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Magnetic resonance imaging–based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

European radiology, 2020-08, Vol.30 (8), p.4615-4622

2020

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CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, p.14534-14543

2022

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Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification

IEEE transaction on neural networks and learning systems, 2023-06, Vol.PP, p.1-15

2023

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Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

Brain Tumor Research and Treatment, 2020, 8(1), , pp.36-42

2020

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Uncertainty-aware semi-supervised few shot segmentation

Pattern recognition, 2023-05, Vol.137, p.109292, Article 109292

2023

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Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features

Expert systems with applications, 2024-12, Vol.256, p.124890, Article 124890

2024

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FedNN: Federated learning on concept drift data using weight and adaptive group normalizations

Pattern recognition, 2024-05, Vol.149, p.110230, Article 110230

2024

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Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets

Medical image analysis, 2024-07, Vol.95, p.103156-103156, Article 103156

2024

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Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, p.10933-10938

2020

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Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, p.200-209

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One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023-10, p.521-531

2023

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A Meta-Learning Approach for Medical Image Registration

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022, p.1-5

2022

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Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

Brain tumor research and treatment, 2020-04, Vol.8 (1), p.36-42

2020

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Magnetic resonance imaging–based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

Uncertainty-aware semi-supervised few shot segmentation

Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features

FedNN: Federated learning on concept drift data using weight and adaptive group normalizations

Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction

One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation

A Meta-Learning Approach for Medical Image Registration

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

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