# An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

arXiv: https://arxiv.org/abs/1806.00712 · HTML: https://mlsanity.com/paper/1806.00712/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 25 of 25)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View](https://mlsanity.com/paper/2112.02625/) (2021)
- [Interpretable Medical Image Classification using Prototype Learning and Privileged Information](https://mlsanity.com/paper/2310.15741/) (2023)
- [The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification](https://mlsanity.com/paper/2108.05386/) (2021)
- [CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models](https://mlsanity.com/paper/2502.05214/) (2025)
- [Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis](https://mlsanity.com/paper/2206.13608/) (2022)
- [Deep Learning Approach for Early Stage Lung Cancer Detection](https://mlsanity.com/paper/2302.02456/) (2023)
- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach](https://mlsanity.com/paper/2308.14000/) (2023)
- [Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images](https://mlsanity.com/paper/2405.17483/) (2024)
- [Deep Learning Neural Network for Lung Cancer Classification: Enhanced Optimization Function](https://mlsanity.com/paper/2208.06353/) (2022)
- [cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis](https://mlsanity.com/paper/2210.16097/) (2022)
- [ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning](https://mlsanity.com/paper/2403.05280/) (2024)
- [Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis](https://mlsanity.com/paper/2411.00609/) (2024)
- [M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification](https://mlsanity.com/paper/2605.12570/) (2026)
- [Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT](https://mlsanity.com/paper/2103.03931/) (2021)
- [Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis](https://mlsanity.com/paper/2204.04219/) (2022)
- [Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2208.00953/) (2022)
- [Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions](https://mlsanity.com/paper/2208.10694/) (2022)
- [Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis](https://mlsanity.com/paper/2406.00555/) (2024)
- [Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization](https://mlsanity.com/paper/2411.06184/) (2024)
- [MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis](https://mlsanity.com/paper/2504.19357/) (2025)
- [LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering](https://mlsanity.com/paper/2505.06370/) (2025)
- [Evaluating Multi-Task Morphological Concept Learning for Pulmonary Nodule Malignancy Assessment in 3D CT](https://mlsanity.com/paper/2609.38271/) (2026)

## Abstract (arXiv)

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches.