Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in 2D medical image understanding, their extension to 3D volumetric imaging remains hindered by prohibitive annotation costs and dataset opacity. Current data formats, predominantly consisting of rigid Visual Question Answering (VQA) pairs or unstructured final clinical reports, typically fail to capture explicit clinical reasoning. To address this limitation, we introduce a large-scale structured reasoning dataset constructed via a novel slice-wise data synthesis paradigm. Inspired by the genuine diagnostic wo
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- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzySimilar title/name (fuzzy) · 59%rasbt/reasoning-from-scratch →
“Fuzzy title match (0.73): “Towards Enhancing 3D Spatial Reasoning in Medical Multimodal” ≈ “rasbt/reasoning-from-scratch””
- LinkedLinked via arxiv author · 85%Zhuoyuan Fu →
“Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Zeshang Li →
“Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Yiqiong Zhang →
“Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Hangui Lin →
“Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models”
- LinkedLinked via arxiv author · 85%Yan Shu →
“Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models”