论文标题
胚胎阶段识别的注意力模型和事后解释方法的比较：一个案例研究
Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study
论文作者
论文摘要
由于深度学习体系结构的复杂性，大多数最先进的模型的基于AI的解决方案（IVF）的开发的一个重要局限性是大多数最先进模型的黑盒性质，这引发了潜在的偏见和公平问题。对可解释的AI的需求不仅在IVF领域，而且在一般的深度学习社区中也增加了。这已经开始了文献趋势，作者专注于设计客观指标来评估通用解释方法。在本文中，我们研究了最近提出的客观忠诚度指标的行为，该指标应用于胚胎阶段识别问题。我们使用指标对注意模型和事后方法进行基准测试，并进一步证明（1）指标对模型排名产生较低的总体一致性，并且（2）取决于公制方法，偏爱事后方法或注意力模型。我们以关于定义忠实的困难以及理解其与受青睐的方法类型的关系的必要性的一般性评论结束。
An important limitation to the development of AI-based solutions for In Vitro Fertilization (IVF) is the black-box nature of most state-of-the-art models, due to the complexity of deep learning architectures, which raises potential bias and fairness issues. The need for interpretable AI has risen not only in the IVF field but also in the deep learning community in general. This has started a trend in literature where authors focus on designing objective metrics to evaluate generic explanation methods. In this paper, we study the behavior of recently proposed objective faithfulness metrics applied to the problem of embryo stage identification. We benchmark attention models and post-hoc methods using metrics and further show empirically that (1) the metrics produce low overall agreement on the model ranking and (2) depending on the metric approach, either post-hoc methods or attention models are favored. We conclude with general remarks about the difficulty of defining faithfulness and the necessity of understanding its relationship with the type of approach that is favored.
