发表机构
unich（那不勒斯费德里克二世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对胎儿超声图像中胼胝体定位难题，提出FedCC框架，集成冻结的DINOv2主干与轻量级YOLO检测头，并引入LoRA模块。在多中心数据集上评估，该框架性能出色，减少了可训练参数与通信成本，迈向可临床部署的胎儿神经超声AI系统。
AI 中文摘要
在胎儿超声图像中准确识别胼胝体对于早期发现神经发育异常至关重要。然而，由于超声成像的固有局限性，包括对比度低、斑点噪声以及胼胝体显著的解剖变异性，该任务极具挑战性。我们提出了FedCC，这是一种基于联邦学习的框架，专门用于胎儿超声图像中的胼胝体定位，适用于现实的多中心和资源受限临床环境，无需数据共享。该框架将冻结的DINOv2主干与基于轻量级YOLO的检测头集成。为实现参数高效适应，引入了低秩适应（LoRA）模块，仅允许一小部分参数在客户端之间优化和交换。此策略大幅降低了计算和通信开销，使框架适用于低资源环境。在一个多中心数据集上进行评估，该数据集包含从三个临床站点的58名孕妇在常规神经超声检查期间使用异构成像设备获取的10970个超声帧。所提出的框架在联邦设置中表现出色。特别是，在FedAvg策略下，DINOv2和LoRA的组合实现了平均mAP@50为0.857和F1分数为0.803，优于完全微调和平编码器冻结基线。值得注意的是，与完全微调中的2440万个参数相比，该方法将可训练参数数量减少到290万个，通信成本降低了约8.5倍。这些发现朝着可扩展、隐私保护且可临床部署的胎儿神经超声人工智能系统迈出了有希望的一步。
英文摘要
Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging, including low contrast, speckle noise, and the considerable anatomical variability of the CC. We propose FedCC, a federated learning (FL)-based framework for CC localization in fetal US images, specifically designed for realistic multi-center and resource-constrained clinical settings without requiring data sharing. The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head. To enable parameter-efficient adaptation, Low-Rank Adaptation (LoRA) modules are incorporated, allowing only a small subset of parameters to be optimized and exchanged among clients. This strategy substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments. The proposed approach was evaluated on a multi-center dataset comprising 10,970 ultrasound frames acquired from 58 pregnant women during routine neurosonographic examinations across three clinical sites using heterogeneous imaging devices. The proposed framework achieved strong performance in the federated setting. In particular, the combination of DINOv2 and LoRA under the FedAvg strategy achieved an average mAP@50 of 0.857 and an F1-score of 0.803, outperforming both full fine-tuning and encoder-freezing baselines. Notably, the proposed approach reduced the number of trainable parameters to 2.9M compared with 24.4M in full fine-tuning, corresponding to an approximately 8.5$\times$ reduction in communication cost. These findings represent a promising step toward scalable, privacy-preserving, and clinically deployable AI systems for fetal neurosonography.
