Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study. (arXiv:2205.06546v1 [cs.CV])

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 …

arxiv attention case case study comparison cv identification stage study