OralMLLM-Bench reveals performance gaps between multimodal large language models and clinicians on cognitive tasks for dental radiographic analysis across periapical, panoramic, and cephalometric images.
Towards understanding sensitive and decisive patterns in explainable ai: a case study of model interpretation in geometric deep learning
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SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.
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OralMLLM-Bench: Evaluating Cognitive Capabilities of Multimodal Large Language Models in Dental Practice
OralMLLM-Bench reveals performance gaps between multimodal large language models and clinicians on cognitive tasks for dental radiographic analysis across periapical, panoramic, and cephalometric images.
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SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.