NeuroHealth-Vision: A Volumetric Perception Module for Multimodal Clinical Reasoning

The NeuroHealth-Vision project aims to bridge the "Protocol Gap" in 3D medical AI by developing a standardized evaluation and transfer-learning framework for volumetric foundation models (VFMs). While current generalist models excel in common thoracic or abdominal tasks, they often struggle with specialized clinical protocols involving unique acquisition physics and contrast kinetics. This project will systematically benchmark frontier architectures (like CT-FM, etc.) across various unseen specialty downstream tasks using zero-shot, few-shot, and Parameter-Efficient Fine-Tuning (PEFT) methodologies. Deliverables include open-source preprocessing pipelines for 3D volumetric normalization and spatial alignment, alongside optimized model weights and a comprehensive benchmarking report. This work will ultimately provide the multimodal "perception engine" for the broader NeuroHealth clinical assistant, enabling it to integrate high-dimensional imaging data into its diagnostic reasoning core.