A synthetic-data-driven, hierarchy-aware adaptation of foundation models produces geometry-consistent representations that improve pose estimation and monocular depth in endoscopy.
arXiv preprint arXiv:2501.05488 (2025)
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RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.
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Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
A synthetic-data-driven, hierarchy-aware adaptation of foundation models produces geometry-consistent representations that improve pose estimation and monocular depth in endoscopy.
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Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis
RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.