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arXiv · 1904.04520

Regression Concept Vectors for Bidirectional Explanations in Histopathology

Mara Graziani·Vincent Andrearczyk·Henning Müller
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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 represents 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 statistical analysis.

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Mara Graziani, Vincent Andrearczyk, Henning Müller. 2019-04-09. Regression Concept Vectors for Bidirectional Explanations in Histopathology. https://arxiv.org/abs/1904.04520

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