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[2602.19822] Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation
Summary
This article presents a novel deep learning framework for efficient endometrial carcinoma screening, utilizing cross-modal synthesis and gradient distillation to enhance diagnostic accuracy in resource-limited settings.
Why It Matters
Early detection of endometrial carcinoma is crucial for effective treatment. This study addresses significant challenges in screening, such as data scarcity and diagnostic reliability, particularly in low-resource environments. The proposed solution could democratize access to high-quality cancer screening.
Key Takeaways
- The proposed framework achieves 99.5% sensitivity and 97.2% specificity in screening.
- Utilizes cross-modal generation to synthesize high-fidelity ultrasound images from MRI data.
- Introduces a lightweight screening network that enhances computational efficiency.
- Addresses class imbalance and subtle imaging features in endometrial carcinoma detection.
- Demonstrates potential for real-time cancer screening in primary care settings.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19822 (cs) [Submitted on 23 Feb 2026] Title:Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation Authors:Dongjing Shan, Yamei Luo, Jiqing Xuan, Lu Huang, Jin Li, Mengchu Yang, Zeyu Chen, Fajin Lv, Yong Tang, Chunxiang Zhang View a PDF of the paper titled Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation, by Dongjing Shan and 9 other authors View PDF HTML (experimental) 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 ultrasou...
