针对甲状腺超声多任务诊断在跨中心数据偏移下的性能下降问题，提出轻量级自适应解码器模块。
Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift
- 设计多核门控适配器，通过互补感受野与语义门控融合多尺度特征。
- 在跨中心数据下，分割精度提升，且在CNN框架中显著提高TI-RADS诊断准确率。
- 适用于医疗影像跨中心迁移场景，尤其适合需兼顾结构与纹理信息的任务。
甲状腺超声自动化同时需要全局几何推理用于结节分割和局部纹理推理用于恶性风险评估。在跨中心域偏移下，这两种线索会不对称退化，但多数多任务模型依赖单一共享主干网络，常引发负迁移。本文分析了基于CNN（ResNet34）和医学ViT（MedSAM）主干的表现，发现ViT更擅长传递几何先验以辅助分割，而CNN在强偏移与伪影条件下更能稳定保留纹理线索用于恶性判断。基于此失败模式，提出轻量级解码器侧适配器——多核门控适配器（MKGA）及其残差变体（ResMKGA），利用互补感受野精炼多尺度跳跃特征，并通过语义上下文条件门控抑制伪影敏感内容后再进行融合。在两个超声基准上，所提适配器提升了跨中心鲁棒性：强化了域外分割性能，在CNN设置下显著提升临床TI-RADS诊断准确率，优于标准多任务基线。代码与模型将公开。
原文摘要 · Abstract (English)
Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reasoning for nodule delineation and local, texture-driven reasoning for malignancy risk assessment. Under cross-center domain shift, these cues degrade asymmetrically, yet most multi-task pipelines rely on a single shared backbone, often inducing negative transfer. In this paper, we characterize this interference across CNN (ResNet34) and medical ViT (MedSAM) backbones, and observe a consistent trend: ViTs transfer geometric priors that benefit segmentation, whereas CNNs more reliably preserve texture cues for malignancy discrimination under strong shift and artifacts. Motivated by this failure mode, we propose a lightweight family of decoder-side adapters, the Multi-Kernel Gated Adapter (MKGA) and a residual variant (ResMKGA), which refine multi-scale skip features using complementary receptive fields and apply semantic, context-conditioned gating to suppress artifact-prone content before fusion. Across two US benchmarks, the proposed adapters improve cross-center robustness: they strengthen out-of-domain segmentation and, in the CNN setting, yield clear gains in clinical TI-RADS diagnostic accuracy compared to standard multi-task baselines. Code and models will be released.
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