Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
Neuroimage54(3), 2033–2044 (2011)
3 Pith papers cite this work. Polarity classification is still indexing.
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CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.
Search-MIND delivers a training-free coarse-to-fine optimization pipeline for multi-modal medical image registration using variance-weighted mutual information and broadened structural descriptors that outperforms ANTs and DINO-reg on liver and abdominal datasets.
citing papers explorer
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Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
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CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.
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Search-MIND: Training-Free Multi-Modal Medical Image Registration
Search-MIND delivers a training-free coarse-to-fine optimization pipeline for multi-modal medical image registration using variance-weighted mutual information and broadened structural descriptors that outperforms ANTs and DINO-reg on liver and abdominal datasets.