An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification

2019/01/18 by Shiwen Shen, Simon X Han, Simon Han +5 · 6 citations
Medicine · #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging #COVID-19 diagnosis using AI

paper · doi:10.1016/j.eswa.2019.01.048

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans
• The Probability of Malignancy in Solitary Pulmonary Nodules
• ImageNet classification with deep convolutional neural networks
• Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
• Densely Connected Convolutional Networks
• TensorFlow: A system for large-scale machine learning

Cited by

• Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Lung Nodule Malignancy Prediction
• Self-explaining AI as an alternative to interpretable AI
• On Interpretability of Artificial Neural Networks: A Survey
• LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering
• MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis
• 3D axial-attention for lung nodule classification