# Regression Concept Vectors for Bidirectional Explanations in Histopathology

## TL;DR

This paper introduces Regression Concept Vectors (RCVs) to interpret deep neural networks in histopathology, enabling concept-based explanations that improve understanding of model sensitivities in medical diagnosis.

## Contribution

The work presents a novel methodology using RCVs for continuous concept measurement in neural network activation spaces, specifically applied to breast cancer grading.

## Key findings

- Nuclei texture is identified as a key concept in tumor detection.
- RCVs provide robust and consistent explanations across samples.
- Directional derivatives along RCVs reveal network sensitivities.

## 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.