HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
Abstract
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, HyperCT outperforms various strong baselines, offering a unified, parameter-efficient solution for holistic patient assessment.
Cite
Text
Liu et al. "HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.Markdown
[Liu et al. "HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.](https://mlanthology.org/midl/2026/liu2026midl-hyperct/)BibTeX
@inproceedings{liu2026midl-hyperct,
title = {{HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis}},
author = {Liu, Fengbei and Kwak, Sunwoo and Phung, Hao and Nizam, Nusrat Binta and Richter, Ilan and Uriel, Nir and Averbuch-Elor, Hadar and Estrin, Deborah and Sabuncu, Mert R.},
booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning},
year = {2026},
pages = {1768-1801},
volume = {315},
url = {https://mlanthology.org/midl/2026/liu2026midl-hyperct/}
}