XAI-reg

Explainability for regression CNN

⭐ The highlight of this work:

• Visualization of explanation methods via saliency maps (different explanation methods).
• Evaluation of explanation methods via perturbation-based method.
• Proposed adatped Area over Perturbation Curve (AOPC) metrics (Samek et al. 2017 ) for regression.
• Comparison of regression models through qualitative (saliency maps) and quantitative (AOPC score) analysis.
• Saliency maps for correct vs incorrect prediction.

💻 About the code:

The code is implimented with Python 3.* and Deep learning library Tensorflow (Keras 2.*) and public library iNNvestigate.

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The work is finished together with Caroline Petitjean and Samia Ainouz in LITIS lab and Florian Yger.
🖼️ Slides
Please consider citing this paper when you use it:

@incollection{zhang2020explainability,
  title={Explainability for regression CNN in fetal head circumference estimation from ultrasound images},
  author={Zhang, Jing and Petitjean, Caroline and Yger, Florian and Ainouz, Samia},
  booktitle={Interpretable and Annotation-Efficient Learning for Medical Image Computing},
  pages={73--82},
  year={2020},
  publisher={Springer}
}