论文标题
胚胎形式：可变形的变压器和协作编码编码为胚胎阶段开发分类
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
论文作者
论文摘要
在体外受精过程（IVF）过程中，早期胚胎中细胞分裂的时间是胚胎生存能力的关键预测指标。但是，在延时监测（TLM）中观察细胞分裂是一个耗时的过程，高度取决于专家。在本文中，我们提出了EmbryosFormer，这是一种计算模型，可以自动检测和对原始延时图像进行分类。我们提出的网络被设计为具有协作负责人的编码器变形变压器。变压器收缩路径可预测每印象标签，并通过分类头进行优化。变压器扩展路径模拟胚胎图像之间的时间相干性，以确保单调非降低约束，并通过分割头进行了优化。合作负责人协同学习了缩合和扩展的道路。我们已经在两个数据集上基准了我们提出的胚胎形式：一个带有8细胞阶段的鼠标胚胎的公共数据集和一个带有4细胞阶段的人类胚胎的内部数据集。源代码：https：//github.com/uark-aicv/embryos。
The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head. Both contracting and expanding paths are synergetically learned by a collaboration head. We have benchmarked our proposed EmbryosFormer on two datasets: a public dataset with mouse embryos with 8-cell stage and an in-house dataset with human embryos with 4-cell stage. Source code: https://github.com/UARK-AICV/Embryos.
