发表机构
China Medical University; China Medical University Hospital（中国医科大学; 中国医科大学附属第一医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出合成CMB生成流程，发现仅用合成数据训练的检测器可达真实数据敏感度的87%，且迁移效果依赖下游架构，揭示了合成数据的潜力与局限。
AI 中文摘要
自动化脑微出血（CMB）检测模型的发展受到CMB低患病率和高昂专家标注成本的阻碍。为解决这一限制，我们开发了一个合成CMB生成流程，并研究了合成病灶用于训练深度学习检测器的有效性。仅使用合成数据训练的模型取得了显著的检测性能，达到了真实数据训练模型病灶敏感度的约87%。我们进一步研究了不同训练范式下合成数据与真实数据的互补作用，揭示了一个显著的性能差距仍然存在。此外，我们发现成功的合成到真实迁移强烈依赖于下游检测架构，这为合成数据在CMB检测中的潜力和局限性提供了新的见解。
英文摘要
The development of automated cerebral microbleed (CMB) detection models is hindered by the low prevalence of CMBs and the high cost of expert annotation. To address this limitation, we developed a synthetic CMB generation pipeline and investigated the effectiveness of synthetic lesions for training deep learning detectors. Models trained solely on synthetic data achieved substantial detection performance, reaching approximately 87% of the lesion sensitivity of their real-trained counterparts. We further investigated the complementary roles of synthetic and real data under different training paradigms, revealing that a significant performance gap remains. Moreover, we found that successful synthetic-to-real transfer is strongly dependent on the downstream detection architecture, providing new insight into both the potential and limitations of synthetic data for CMB detection.
