Non-contrast chest CTs offer a rich opportunity for both conventional pulmonary and opportunistic extra-pulmonary screening. While Multi-Task Learning (MTL) can unify these diverse tasks, standard hard-parameter sharing approaches are often suboptimal for modeling distinct pathologies. We propose HyperCT, a framework that dynamically adapts a Vision Transformer backbone via a Hypernetwork. To ensure computational efficiency, we integrate Low-Rank Adaptation (LoRA), allowing the model to regress task-specific low-rank weight updates rather than full parameters. Validated on a large-scale dataset of radiological and cardiological tasks, \method{} outperforms various strong baselines, offering a unified, parameter-efficient solution for holistic patient assessment. Our code is available at https://github.com/lfb-1/HyperCT.
@article{arxiv.2604.03224,
title = {HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis},
author = {Fengbei Liu and Sunwoo Kwak and Hao Phung and Nusrat Binta Nizam and Ilan Richter and Nir Uriel and Hadar Averbuch-Elor and Daborah Estrin and Mert R. Sabuncu},
journal= {arXiv preprint arXiv:2604.03224},
year = {2026}
}