Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images
Not on the map. Why?
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.
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
24 of 45 references.
References · 24
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
- 2018Sanity Checks for Saliency Mapscited by 1,156
- 2020Concept Bottleneck Modelscited by 1,032
- 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 737
- 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
- 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
- 2019Understanding Deep Networks via Extremal Perturbations and Smooth Maskscited by 259
- 2020Concept Whitening for Interpretable Image Recognitioncited by 225
12 more
- 2018Grounding Visual Explanationscited by 93
- 2020GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionscited by 82
- 2017MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Networkcited by 56
- 2020Now You See Me (CME): Concept-based Model Extractioncited by 51
- 2020On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectorscited by 39
- 2019Regression Concept Vectors for Bidirectional Explanations in Histopathologycited by 31
- 2018An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classificationcited by 25
- 2018Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Predictioncited by 16
- 2021Comprehensible Convolutional Neural Networks via Guided Concept Learningcited by 11
- 2019Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipelinecited by 5
- 2020Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitiscited by 5
- 2020Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNscited by 4
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.