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@ARTICLE{,
    author = {Graziani, Mara and Andrearczyk, Vincent and M{\"{u}}ller, Henning},
  keywords = {concept vectors, Deep convolutional neural network, Deep Learning, Histopathology, interpretability},
     title = {Regression Concept Vectors for Bidirectional Explanations in Histopathology},
   journal = {Lecture Notes in Computer Science},
    volume = {Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2018},
      year = {2018},
     pages = {8},
      note = {Source code: https://github.com/medgift/iMIMIC-RCVs},
  abstract = {Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making. In this work, we propose a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer. The directional derivative of the decision function along the RCVs rep- resents the network sensitivity to increasing values of a given concept measure. When applied to breast cancer grading, nuclei texture emerges as a relevant concept in the detection of tumor tissue in breast lymph node samples. We evaluate score robustness and consistency by statisti- cal analysis.}
}