Chengxin Yang — accepted AI-conference papers

2026

Diffusion-Based Native Adversarial Synthesis for Enhanced Medical Segmentation Generalization

Hongyu Zhang, Haipeng Chen, Zhimin Xu, Chengxin Yang, Yingda Lyu

CVPR 2026

Diffusion models (DMs) can generate anatomically realistic medical images, offering a compelling route to improving generalization through synthetic augmentation. Yet high visual realism does not necessarily translate into improved downstream utility. This work addresses two key questions in diffusi

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2026

VGD: Value-Guided Diffusion Toward High-Utility Medical Image Segmentation

Hongyu Zhang, Haipeng Chen, Chengxin Yang, Yingda Lyu

AAAI 2026technical

Progress in medical image segmentation is fundamentally constrained by the scarcity of annotated data. While diffusion models offer a promising solution by generating high-fidelity image–mask pairs, their utility for downstream tasks remains underexplored. A key bottleneck lies in the misalignment

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