轻量级3D医学图像分割模型通过新知识蒸馏方法逼近大模型性能。
ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation
- 用区域和上下文感知蒸馏，保留重要解剖细节与长距离依赖
- 在多个数据集上参数减少90%以上，推理延迟降低60%以上
- 无需定制学生网络，可适配多种架构，适合临床部署
精准的3D医学图像分割对诊断与治疗规划至关重要，但当前先进模型通常过于庞大，难以在计算资源有限的医疗机构部署。轻量级架构往往伴随显著性能下降。为解决部署与速度限制，我们提出区域与上下文感知知识蒸馏（ReCo-KD），一种仅需训练的框架，可将高容量教师模型中的细粒度解剖结构信息与长程上下文信息迁移至紧凑的学生网络。该框架融合多尺度结构感知区域蒸馏（MS-SARD），通过类别感知掩码与尺度归一化加权，强化小但临床关键区域；以及多尺度上下文对齐（MS-CA），在特征层级间对齐师生亲和模式。在nnU-Net上以骨干无关方式实现，无需定制学生网络，可轻松适配其他架构。在多个公开3D医学分割数据集及一个挑战性聚合数据集上的实验表明，经蒸馏的轻量模型精度接近教师模型，同时参数大幅减少、推理延迟显著降低，凸显其临床部署实用性。
原文摘要 · Abstract (English)
Accurate 3D medical image segmentation is vital for diagnosis and treatment planning, but state-of-the-art models are often too large for clinics with limited computing resources. Lightweight architectures typically suffer significant performance loss. To address these deployment and speed constraints, we propose Region- and Context-aware Knowledge Distillation (ReCo-KD), a training-only framework that transfers both fine-grained anatomical detail and long-range contextual information from a high-capacity teacher to a compact student network. The framework integrates Multi-Scale Structure-Aware Region Distillation (MS-SARD), which applies class-aware masks and scale-normalized weighting to emphasize small but clinically important regions, and Multi-Scale Context Alignment (MS-CA), which aligns teacher-student affinity patterns across feature levels. Implemented on nnU-Net in a backbone-agnostic manner, ReCo-KD requires no custom student design and is easily adapted to other architectures. Experiments on multiple public 3D medical segmentation datasets and a challenging aggregated dataset show that the distilled lightweight model attains accuracy close to the teacher while markedly reducing parameters and inference latency, underscoring its practicality for clinical deployment.
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