Regression Concept Vectors (RCVs)

This repository contains the development of the interpretability and perturbation robustness for the analysis of histopathology images by convolutional neural networks.

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57 commits | Last update: October 29, 2020

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What Regression Concept Vectors (RCVs) can do for you

  • Extraction of concept measures from manual nuclei annotations
  • Correlation analysis between concepts and network prediction
  • Analysis of the concept learning dynamics during training
  • Perturbation analysis by bidirectional relevance scores

Recent work on Testing with Concept Activation Vectors (TCAV) proposed directional derivatives to quantify the in uence of user-defined concepts on the network output [1]. However, diagnostic concepts are often continuous measures that might be counter intuitive to describe by their presence or absence. In this paper, we extend TCAV from a classification problem to a regression problem by computing Regression Concept Vectors (RCVs). Instead of seeking a discriminator between two concepts, we seek the direction of greatest increase of the measures for a single continuous concept. We measure the relevance of a concept with bidirectional relevance scores, Br. The Br scores assume positive values when increasing values of the concept measures positively affect classification and negative in the opposite case.

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Tags
  • Medical image data
  • Machine learning
Programming Language
  • Python
License
  • MIT
Source code

Participating organizations

Contributors

  • Mara Graziani
    Haute École Spécialisée de Suisse Occidentale HES-SO Valais (HESSO)
Contact person
Mara Graziani
Haute École Spécialisée de Suisse Occidentale HES-SO Valais (HESSO)

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