ArticleDiagnostic pathology2023
Explainable convolutional neural networks for assessing head and neck cancer histopathology.
Article in Diagnostic pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- Application of artificial intelligence in early warning, diagnosis and precise treatment of head and neck tumors (Review).Molecular and clinical oncology · 2026Review
- Article
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- Deep learning-based multimodal fusion of MRI and whole slide image for predicting neoadjuvant therapy response in locally advanced head and neck squamous cell carcinoma.BMC medical imaging · 2026Article
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- Article
- Interpretable AI-assisted diagnosis of papillary thyroid cancer cytopathology using graph neural networks and knowledge graphs.Scientific reports · 2025Article
- The Limitations of Artificial Intelligence in Head and Neck Oncology.Advances in therapy · 2025Review
- Article
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
- Responsible artificial intelligence for addressing equity in oral healthcare.Frontiers in oral health · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeAlthough neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI.
methodsHere, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology. To this end, we manually annotated 101 histopathological slides of locally advanced head and neck squamous cell carcinoma. We trained a CNN to classify tumor and non-tumor tissue, and another CNN to semantically segment four classes - tumor, non-tumor, non-specified tissue, and background. We applied Explainable AI techniques, namely Grad-CAM and HR-CAM, to both networks and explored important features that contributed to their decisions.
resultsThe classification network achieved an accuracy of 89.9% on previously unseen data. Our segmentation network achieved a class-averaged Intersection over Union score of 0.690, and 0.782 for tumor tissue in particular. Explainable AI methods demonstrated that both networks rely on features agreeing with the pathologist's expert opinion.
conclusionOur work suggests that CNNs can predict head and neck cancer with high accuracy. Especially if accompanied by visual explanations, CNNs seem promising for assisting pathologists in the assessment of cancer sections.
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Registered trials
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