ArticleExpert systems with applications2019
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification.
Article in Expert systems with applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- LNMSNet: a multi-task deep learning network for pulmonary nodules segmentation and malignancy classification.Frontiers in medicine · 2026Article
- Enhanced differentiation of breast lesions through integration of microvascular flow imaging and machine learning algorithms.Frontiers in oncology · 2026Article
- Vision-language model-based semantic-guided imaging biomarker for lung nodule malignancy prediction.Journal of biomedical informatics · 2025Article
- Hierarchical random forest model, inflammation and oxidative stress as predictors of the atherogenic index of plasma and diabetes progression.Scientific reports · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis.Scientific reports · 2025Article
- Multitask Swin Transformer for classification and characterization of pulmonary nodules in CT images.Quantitative imaging in medicine and surgery · 2025Article
- Proto-Caps: interpretable medical image classification using prototype learning and privileged information.PeerJ. Computer science · 2025Article
- Hierarchical classification of early microscopic lung nodule based on cascade network.Health information science and systems · 2024Article
- An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases.Biomimetics (Basel, Switzerland) · 2024Article
- Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.Scientific reports · 2024Article
- Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis.BioData mining · 2024Article
- Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.Cancer medicine · 2024Review
- A semantic fidelity interpretable-assisted decision model for lung nodule classification.International journal of computer assisted radiology and surgery · 2024Article
- Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.Journal of translational medicine · 2024Article
- A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers.Journal of applied clinical medical physics · 2023Article
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5 authors.
Funding
Abstract
While deep learning methods have demonstrated performance comparable to human readers in 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 end 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 semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. 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 using a 3D CNN alone.
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