ArticleCancers2025
Multimodal Deep Learning for Stage Classification of Head and Neck Cancer Using Masked Autoencoders and Vision Transformers with Attention-Based Fusion.
Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Head and Neck Surgical Oncology: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Leveraging vision transformer for histological grade prediction in laryngeal and hypopharyngeal squamous cell carcinoma: a large-scale multicenter study.Neuroradiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a prevalent and aggressive cancer, and accurate staging using the AJCC system is essential for treatment planning. This study aims to enhance AJCC staging by integrating both clinical and imaging data using a multimodal deep learning pipeline. We propose a framework that employs a VGG16-based masked autoencoder (MAE) for self-supervised visual feature learning, enhanced by attention mechanisms (CBAM and BAM), and fuses image and clinical features using an attention-weighted fusion network. The models, benchmarked on the HNSCC and HN1 datasets, achieved approximately 80% accuracy (four classes) and ~66% accuracy (five classes), with notable AUC improvements, especially under BAM. The integration of clinical features significantly enhances stage-classification performance, setting a precedent for robust multimodal pipelines in radiomics-based oncology applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.