# Regression Concept Vectors for Bidirectional Explanations in Histopathology

arXiv: https://arxiv.org/abs/1904.04520 · HTML: https://mlsanity.com/paper/1904.04520/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 30 of 31)

- [Acquisition of Chess Knowledge in AlphaZero](https://mlsanity.com/paper/2111.09259/) (2021)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Concept Activation Regions: A Generalized Framework For Concept-Based Explanations](https://mlsanity.com/paper/2209.11222/) (2022)
- [LINe: Out-of-Distribution Detection by Leveraging Important Neurons](https://mlsanity.com/paper/2303.13995/) (2023)
- [Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis](https://mlsanity.com/paper/2304.04579/) (2023)
- [Human-Centered Concept Explanations for Neural Networks](https://mlsanity.com/paper/2202.12451/) (2022)
- [Best of both worlds: local and global explanations with human-understandable concepts](https://mlsanity.com/paper/2106.08641/) (2021)
- [Concept Embedding Analysis: A Review](https://mlsanity.com/paper/2203.13909/) (2022)
- [Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?](https://mlsanity.com/paper/2409.13456/) (2024)
- [HINT: Hierarchical Neuron Concept Explainer](https://mlsanity.com/paper/2203.14196/) (2022)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification](https://mlsanity.com/paper/2411.05698/) (2024)
- [Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability](https://mlsanity.com/paper/2304.14864/) (2023)
- [Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools](https://mlsanity.com/paper/2110.07120/) (2021)
- [Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology](https://mlsanity.com/paper/2501.02922/) (2025)
- [Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces](https://mlsanity.com/paper/2311.14435/) (2023)
- [Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information](https://mlsanity.com/paper/2210.10689/) (2022)
- [Uncovering Unique Concept Vectors through Latent Space Decomposition](https://mlsanity.com/paper/2307.06913/) (2023)
- [FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks](https://mlsanity.com/paper/2505.17883/) (2025)
- [Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding](https://mlsanity.com/paper/2112.11805/) (2021)
- [Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers](https://mlsanity.com/paper/2307.01900/) (2023)
- [Scalable Concept Extraction in Industry 4.0](https://mlsanity.com/paper/2306.03551/) (2023)
- [Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images](https://mlsanity.com/paper/2405.17483/) (2024)
- [In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging](https://mlsanity.com/paper/2508.13880/) (2025)
- [On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs](https://mlsanity.com/paper/2504.08602/) (2025)
- [Concept-Based Interpretability for Toxicity Detection](https://mlsanity.com/paper/2511.16689/) (2025)
- [LAP: An Attention-Based Module for Concept Based Self-Interpretation and Knowledge Injection in Convolutional Neural Networks](https://mlsanity.com/paper/2201.11808/) (2022)
- [Enhancing Performance of Explainable AI Models with Constrained Concept Refinement](https://mlsanity.com/paper/2502.06775/) (2025)
- [Attack logics, not outputs: Towards efficient robustification of deep neural networks by falsifying concept-based properties](https://mlsanity.com/paper/2510.03320/) (2025)
- [MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space](https://mlsanity.com/paper/2605.20549/) (2026)

## Abstract (arXiv)

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.