Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation

TL;DR

This paper introduces a novel two-stage deep learning framework that synthesizes ultrasound images from MRI data and employs gradient distillation to enable highly accurate, resource-efficient endometrial carcinoma screening in primary care settings.

Contribution

The study presents a structure-guided cross-modal synthesis network and a lightweight gradient distillation-based screening model, addressing data scarcity and computational constraints in EC detection.

Findings

Achieved 99.5% sensitivity and 97.2% specificity in multicenter cohort

Model outperforms expert sonographers in diagnostic accuracy

Operates at a minimal computational cost of 0.289 GFLOPs

Abstract

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.