# Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images

arXiv: https://arxiv.org/abs/2405.17483 · HTML: https://mlsanity.com/paper/2405.17483/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Understanding Deep Networks via Extremal Perturbations and Smooth Masks](https://mlsanity.com/paper/1910.08485/) (2019)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [Grounding Visual Explanations](https://mlsanity.com/paper/1807.09685/) (2018)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network](https://mlsanity.com/paper/1707.02485/) (2017)
- [Now You See Me (CME): Concept-based Model Extraction](https://mlsanity.com/paper/2010.13233/) (2020)
- [On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors](https://mlsanity.com/paper/2005.02000/) (2020)
- [Regression Concept Vectors for Bidirectional Explanations in Histopathology](https://mlsanity.com/paper/1904.04520/) (2019)
- [An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification](https://mlsanity.com/paper/1806.00712/) (2018)
- [Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction](https://mlsanity.com/paper/1802.03584/) (2018)
- [Comprehensible Convolutional Neural Networks via Guided Concept Learning](https://mlsanity.com/paper/2101.03919/) (2021)
- [Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline](https://mlsanity.com/paper/1912.08679/) (2019)
- [Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis](https://mlsanity.com/paper/2006.02666/) (2020)
- [Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs](https://mlsanity.com/paper/2010.11682/) (2020)

## Built on by (most cited first, 2 of 2)

- [M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification](https://mlsanity.com/paper/2605.12570/) (2026)
- [Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges](https://mlsanity.com/paper/2503.19949/) (2025)

## Abstract (arXiv)

To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on applying the generalized additive models and the concept-based learning is proposed. The model detects a set of clinically significant attributes in addition to the final malignancy regression score and learns the association between the lung nodule attributes and a final diagnosis decision as well as their contributions into the decision. The proposed concept-based learning framework provides human-readable explanations in terms of different concepts (numerical and categorical), their values, and their contribution to the final prediction. Numerical experiments with the LIDC-IDRI dataset demonstrate that the diagnosis results obtained using the proposed model, which explicitly explores internal relationships, are in line with similar patterns observed in clinical practice. Additionally, the proposed model shows the competitive classification and the nodule attribute scoring performance, highlighting its potential for effective decision-making in the lung nodule diagnosis.