用低秩超网络动态适配视觉Transformer，统一肺部与心臟影像分析。
HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
- 通过超网络动态生成任务专属的低秩权重更新。
- 在多任务胸部CT数据集上超越多个强基线模型。
- 适合需要高效多病种筛查的临床医学研究者。
非增强胸部CT为肺部常规检查及偶然性心外病变筛查提供了丰富机会。虽然多任务学习（MTL）能统一这些多样化任务，但传统的硬参数共享方法往往难以有效建模不同病理特征。我们提出HyperCT，一种通过超网络动态适配视觉Transformer主干网络的框架。为保证计算效率，引入低秩适应（LoRA），使模型仅需回归任务特定的低秩权重更新，而非完整参数。在大规模放射科与心脏病学任务数据集上验证，该方法显著优于多种强基线模型，提供了一种统一且参数高效的患者整体评估方案。代码已开源。
原文摘要 · Abstract (English)
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.
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