English(EN) AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading

AI框架提高了IVF中胚胎分级的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-04 04:00

研究人员开发了一种名为AttnRegDeepLab的新框架，用于对IVF过程中的胚胎碎片进行分级。这种两阶段、双分支系统使用注意力门来提高分割精度，减少噪声，并结合多尺度回归头来纠正估计误差。该方法旨在提供一种临床上可解释的解决方案，平衡视觉保真度和量化精度，优于端到端方法。 AI

影响该AI框架提供了一种更精确、更具可解释性的胚胎碎片分级方法，有望提高IVF成功率。

排序理由这是一篇详细介绍特定应用新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Ming-Jhe Lee, Chang-Hong Wu, Jung-Hua Wang, Ming-Jer Chen, Yu-Chiao Yi, Tsung-Hsien Lee · 2026-06-04 04:00

AttnRegDeepLab：一种用于可解释胚胎碎片分级的两阶段解耦框架

arXiv:2511.18454v3 Announce Type: replace-cross Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF). However, manual grading is subjective and inefficient, while existing deep learning solutio…