Early detection of myometrial invasion is critical for the staging and life-saving management of endometrial carcinoma (EC), a prevalent global malignancy. Transvaginal ultrasound serves as the primary, accessible screening modality in resource-constrained primary care settings; however, its diagnostic reliability is severely hindered by low tissue contrast, high operator dependence, and a pronounced scarcity of positive pathological samples. Existing artificial intelligence solutions struggle to overcome this severe class imbalance and the subtle imaging features of invasion, particularly under the strict computational limits of primary care clinics. Here we present an automated, highly efficient two-stage deep learning framework that resolves both data and computational bottlenecks in EC screening. To mitigate pathological data scarcity, we develop a structure-guided cross-modal generation network that synthesizes diverse, high-fidelity ultrasound images from unpaired magnetic resonance imaging (MRI) data, strictly preserving clinically essential anatomical junctions. Furthermore, we introduce a lightweight screening network utilizing gradient distillation, which transfers discriminative knowledge from a high-capacity teacher model to dynamically guide sparse attention towards task-critical regions. Evaluated on a large, multicenter cohort of 7,951 participants, our model achieves a sensitivity of 99.5\%, a specificity of 97.2\%, and an area under the curve of 0.987 at a minimal computational cost (0.289 GFLOPs), substantially outperforming the average diagnostic accuracy of expert sonographers. Our approach demonstrates that combining cross-modal synthetic augmentation with knowledge-driven efficient modeling can democratize expert-level, real-time cancer screening for resource-constrained primary care settings.

翻译：早期检测肌层浸润对于子宫内膜癌（一种全球高发恶性肿瘤）的分期及挽救生命的治疗至关重要。经阴道超声作为资源有限初级医疗机构中主要且可及的筛查手段，其诊断可靠性却因组织对比度低、操作者依赖性高以及阳性病理样本极度匮乏而受到严重制约。现有的人工智能解决方案难以克服这种严重的类别不平衡问题以及浸润征象的细微影像学特征，尤其在初级诊所严格的计算资源限制下。本文提出一种自动化、高效率的两阶段深度学习框架，以解决子宫内膜癌筛查中的数据与计算瓶颈。为缓解病理数据稀缺问题，我们开发了一种结构引导的跨模态生成网络，该网络从未配对的磁共振成像数据中合成多样化、高保真度的超声图像，并严格保留临床必需的解剖交界结构。此外，我们引入了一种采用梯度蒸馏的轻量化筛查网络，通过从高容量教师模型中迁移判别性知识，动态引导稀疏注意力聚焦于任务关键区域。在包含7,951名参与者的大型多中心队列上进行评估，我们的模型以极低计算成本（0.289 GFLOPs）实现了99.5%的敏感性、97.2%的特异性及0.987的曲线下面积，显著超越了超声专家平均诊断准确率。我们的研究表明，将跨模态合成增强与知识驱动的高效建模相结合，可为资源有限的初级医疗机构实现专家级实时癌症筛查的普及化。