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Deep learning interpretability is very important, especially in medical imaging where clinical results can be greatly impacted by an understanding of model decisions. The application of variational autoencoders (VAEs) as post hoc interpretability tools for deep networks is investigated in this research. We show feature importance and examine model behavior through latent space perturbations by utilizing the latent space that VAEs have learned. The VAEs performance is evaluated and compared with numerous popular post-hoc interpretability techniques in this study for the Breast Cancer Histopathological Images. The comparison through RemOve And Retrain (ROAR) shows that the VAE based tool performs better in most cases and is the fastest among all the available post hoc interpretability methods.

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Post Hoc Interpretability of Deep Learning Models for Breast Cancer Histopathological Images with Variational Autoencoders

  • 会议论文

  • 发表日期：

    2025年

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  • Muhammad Waqas,
  • Tomas Maul,
  • Iman Yi Liao,
  • Amr Ahmed

摘要

Deep learning interpretability is very important, especially in medical imaging where clinical results can be greatly impacted by an understanding of model decisions. The application of variational autoencoders (VAEs) as post hoc interpretability tools for deep networks is investigated in this research. We show feature importance and examine model behavior through latent space perturbations by utilizing the latent space that VAEs have learned. The VAEs performance is evaluated and compared with numerous popular post-hoc interpretability techniques in this study for the Breast Cancer Histopathological Images. The comparison through RemOve And Retrain (ROAR) shows that the VAE based tool performs better in most cases and is the fastest among all the available post hoc interpretability methods.