用多任务嵌入模型自动识别胚胎关键结构并分级，提升试管婴儿评估一致性。
Multitasking Embedding for Embryo Blastocyst Grading Prediction (MEmEBG)
- 基于预训练ResNet-18加嵌入层，联合学习胚胎内外细胞层特征。
- 在有限数据下实现对滋养层和内细胞团的精准定位与等级预测。
- 适合辅助生殖领域医生和算法研发者参考，推动胚胎评估标准化。
可靠的胚胎质量评估对体外受精（IVF）成功至关重要。当前胚胎评级主要依赖形态学的视觉判断，存在主观性强、不同医生间差异大及质量控制难等问题。本研究提出一种基于多任务嵌入的方法，用于自动化分析和预测第5天人类胚胎的关键成分：滋养外胚层（TE）、内细胞团（ICM）及囊胚扩张程度（EXP）。该方法利用图像中提取的生物学与物理特征，采用预训练的ResNet-18架构并加入嵌入层，从有限数据中学习判别性表示，自动识别视觉上相似且难以区分的TE与ICM区域及其对应等级。实验结果表明，该多任务嵌入方法在胚胎质量评估中具有潜力，可实现更稳健、一致的评估效果。
原文摘要 · Abstract (English)
Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices primarily rely on visual assessment of morphological features, which introduces subjectivity, inter-embryologist variability, and challenges in standardizing quality assurance. In this study, we propose a multitask embedding-based approach for the automated analysis and prediction of key blastocyst components, including the trophectoderm (TE), inner cell mass (ICM), and blastocyst expansion (EXP). The method leverages biological and physical characteristics extracted from images of day-5 human embryos. A pretrained ResNet-18 architecture, enhanced with an embedding layer, is employed to learn discriminative representations from a limited dataset and to automatically identify TE and ICM regions along with their corresponding grades, structures that are visually similar and inherently difficult to distinguish. Experimental results demonstrate the promise of the multitask embedding approach and potential for robust and consistent blastocyst quality assessment.
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