HyperCT：用于统一胸部CT分析的低秩超网络
HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
AI总结：
HyperCT通过动态调整视觉Transformer骨干网络，结合低秩适应技术，实现高效的多任务统一分析，优于现有基线方法。
摘要：
非对比胸部CT为传统肺部和机会性非肺部筛查提供了丰富的机会。虽然多任务学习（MTL）可以统一这些多样化任务，但标准硬参数共享方法在建模不同病理学时往往效果不佳。我们提出了HyperCT框架，通过超网络动态调整视觉Transformer骨干网络。为确保计算效率，我们整合了低秩适应（LoRA），使模型能够回归任务特定的低秩权重更新，而不是完整参数。在大规模放射学和心血管学任务数据集上验证，方法在性能上优于各种强基线，提供了一种统一且参数高效的患者评估解决方案。我们的代码可在https://github.com/lfb-1/HyperCT获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
