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A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images

https://doi.org/10.1002/mp.16680
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-28954-6_14openalex-snapshot
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inferredcites →https://doi.org/10.1007/s10278-019-00309-wopenalex-snapshot
Classifying Neck Lymph Nodes of Head and Neck Squamous Cell Carcinoma in MRI Images with Radiomic Features
inferredcites →https://doi.org/10.1007/s11282-020-00449-8openalex-snapshot
Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study
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Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging
inferredcites →https://doi.org/10.1016/j.ijrobp.2020.10.005openalex-snapshot
Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach
inferredcites →https://doi.org/10.1016/j.oooo.2018.10.002openalex-snapshot
Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence
inferredcites →https://doi.org/10.1038/s41598-018-32441-yopenalex-snapshot
Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks
inferredcites →https://doi.org/10.1088/1361-6560/ab083aopenalex-snapshot
Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer
inferredcites →https://doi.org/10.1093/jnci/djs491openalex-snapshot
Annual Report to the Nation on the Status of Cancer, 1975–2009, Featuring the Burden and Trends in Human Papillomavirus (HPV)–Associated Cancers and HPV Vaccination Coverage Levels
inferredcites →https://doi.org/10.1109/bibm49941.2020.9313482openalex-snapshot
3D Texture Feature-Based Lymph Node Automated Detection in Head and Neck Cancer Analysis
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.266openalex-snapshot
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.1995openalex-snapshot
Proceedings of IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot
Fast R-CNN
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/msp.2022.3142719openalex-snapshot
Robust Explainability: A tutorial on gradient-based attribution methods for deep neural networks
inferredcites →https://doi.org/10.1117/12.2043737openalex-snapshot
Mediastinal lymph node detection on thoracic CT scans using spatial prior from multi-atlas label fusion
inferredcites →https://doi.org/10.1117/12.2043837openalex-snapshot
Abdominal lymphadenopathy detection using random forest
inferredcites →https://doi.org/10.1145/2649387openalex-snapshot
Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3459930.3469561openalex-snapshot
Extracapsular extension identification for head and neck cancer using multi-scale 3D deep neural network
inferredcites →https://doi.org/10.1200/jco.19.02031openalex-snapshot
Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma
inferredcites →https://doi.org/10.3389/fninf.2019.00061openalex-snapshot
Recommendations for Processing Head CT Data
inferredcites →https://doi.org/10.3390/cancers13040600openalex-snapshot
Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma
inferredcites →https://doi.org/10.3390/jimaging6060052openalex-snapshot
Explainable Deep Learning Models in Medical Image Analysis
inferredcites →https://doi.org/10.48550/arxiv.1312.6034openalex-snapshot
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6806openalex-snapshot
Striving for Simplicity: The All Convolutional Net
inferredcites →https://doi.org/10.48550/arxiv.1605.01713openalex-snapshot
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
inferredcites →https://doi.org/10.48550/arxiv.1708.08296openalex-snapshot
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
inferredcites →https://doi.org/10.48550/arxiv.2005.13705openalex-snapshot
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
inferredcites →https://doi.org/10.7759/cureus.34769openalex-snapshot
Preoperative Prediction and Identification of Extracapsular Extension in Head and Neck Cancer Patients: Progress and Potential

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/lary.31756openalex-snapshot
Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology
inferred← citeshttps://doi.org/10.1002/lary.70194openalex-snapshot
Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis
inferred← citeshttps://doi.org/10.1002/widm.70075openalex-snapshot
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
inferred← citeshttps://doi.org/10.1016/j.artmed.2025.103243openalex-snapshot
Leveraging explainable artificial intelligence for transparent and trustworthy cancer detection systems
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105689openalex-snapshot
Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review
inferred← citeshttps://doi.org/10.1016/j.oraloncology.2026.107862openalex-snapshot
Prognostic significance of extranodal extension in cervical lymph node metastases of head and neck squamous cell carcinoma: A systematic review and meta-analysis with bibliometric overview
inferred← citeshttps://doi.org/10.1038/s41598-024-84836-9openalex-snapshot
Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification
inferred← citeshttps://doi.org/10.1038/s41598-025-14917-wopenalex-snapshot
Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets
inferred← citeshttps://doi.org/10.1109/icdmw65004.2024.00120openalex-snapshot
Automated Detection and Quantitative Assessment of Extranodal Extension in Lymph Node Breast Tumors Using Dual-Tier Deep Learning and Density-Based Contour Algorithms
inferred← citeshttps://doi.org/10.1109/isbi61048.2026.11515601openalex-snapshot
DA-GMGENet: A Domain Adaptation Framework for 3D CT-Based ECE Identification in Head and Neck Cancer
inferred← citeshttps://doi.org/10.1186/s12903-025-07543-5openalex-snapshot
Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis
inferred← citeshttps://doi.org/10.3390/cancers17162711openalex-snapshot
Machine Learning-Based Classification of Cervical Lymph Nodes in HNSCC: A Radiomics Approach with Feature Selection Optimization
inferred← citeshttps://doi.org/10.7759/cureus.34769openalex-snapshot
Preoperative Prediction and Identification of Extracapsular Extension in Head and Neck Cancer Patients: Progress and Potential
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