Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare. It works by minimizing the distance between matched (positive) data modalities, while maximizing the distance between mismatched (negative) samples. Traditional CL frameworks typically assume instance-based correspondence within data batches, treating all non-paired samples as negatives. However, this assumption often fails in medical settings, where samples may share high-level semantic attributes, leading t
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- PossiblePossibly related (embedding) · 46%PnP-CoSMo: A Multi-Contrast MRI Reconstruction Framework based on Content/Style Modeling [R] →
- FuzzySimilar title/name (fuzzy) · 59%vllm-project/semantic-router →
“Fuzzy title match (0.73): “Multimodal Semantic-Aware Contrastive Learning For False Neg” ≈ “vllm-project/semantic-router””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Multimodal Semantic-Aware Contrastive Learning For False Neg” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Multimodal Semantic-Aware Contrastive Learning For False Neg” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Sara Ketabi →
“Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging”
- LinkedLinked via arxiv author · 85%Matthias W. Wagner →
“Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging”
- LinkedLinked via arxiv author · 85%Cynthia Hawkins →
“Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging”
- LinkedLinked via arxiv author · 85%Uri Tabori →
“Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging”