BLEG enhances GNNs for fMRI brain network analysis by prompting LLMs for text augmentation, using cost-effective instruction tuning, and applying alignment losses during joint training.
Med-moe: Mixture of domain-specific experts for lightweight medical vision-language models
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Transferring a 2D MLLM to 3D CT inputs via parameter reuse, a Text-Guided Hierarchical MoE framework, and two-stage training yields better performance than prior 3D medical MLLMs on medical report generation and visual question answering.
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
A literature survey that categorizes how Mixture-of-Experts architectures address multimodal learning challenges and identifies open research gaps.
CSA-MoE-Net achieves 96.33% accuracy, 94.09% precision, 98.53% recall, 96.25% F1-score and 99.50% AUC on 2,129 balanced breast ultrasound images, improving over ResNet-18 by 3.01 to 5.42 percentage points.
citing papers explorer
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BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis
BLEG enhances GNNs for fMRI brain network analysis by prompting LLMs for text augmentation, using cost-effective instruction tuning, and applying alignment losses during joint training.
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Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis
Transferring a 2D MLLM to 3D CT inputs via parameter reuse, a Text-Guided Hierarchical MoE framework, and two-stage training yields better performance than prior 3D medical MLLMs on medical report generation and visual question answering.
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HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding
HeartcareGPT proposes Dual Stream Projection Alignment (DSPA) on a structure-aware tokenizer for unified ECG signal-image modeling, supported by Heartcare-400K dataset and Heartcare-Bench.
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Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
A literature survey that categorizes how Mixture-of-Experts architectures address multimodal learning challenges and identifies open research gaps.
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Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis
CSA-MoE-Net achieves 96.33% accuracy, 94.09% precision, 98.53% recall, 96.25% F1-score and 99.50% AUC on 2,129 balanced breast ultrasound images, improving over ResNet-18 by 3.01 to 5.42 percentage points.