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Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods

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https://doi.org/10.54963/entu.v15i1.871
Majeed, T., & Assad, A. (2025). Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods. ENT Updates, 15(1). https://doi.org/10.54963/entu.v15i1.871
Volume 15 Issue 1 (2025)
Copyright (c) 2025 Tabasum Majeed, Assif Assad
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Head and neck cancers (HNCs) include malignancies of the oral cavity, salivary glands, thyroid, oropharynx, and nasopharynx, with risk factors such as tobacco use, alcohol consumption, viral infections, and environmental exposures contributing to over half a million global cases annually. Despite treatment advances, poor prognosis underscores the need for accurate diagnosis and continuous monitoring. Medical imaging plays a critical role in HNC evaluation but is often limited by the complexity of anatomy and tumor biology. Recent advances in artificial intelligence (AI), particularly deep learning, offer opportunities to enhance diagnostic accuracy and optimize treatment strategies. This study reviews the application of deep learning in HNC imaging, evaluating different architectures and addressing challenges like limited annotated datasets, high computational demands, and ethical concerns. Overcoming these challenges will revolutionize HNC diagnostics, redefine precision oncology, and improve patient care. The future integration of explainable AI models and multimodal data will be crucial in advancing diagnostic precision, ensuring clinical applicability, and addressing ethical and resource challenges. As AI progresses, its effective integration into clinical workflows will not only enhance healthcare delivery but also reduce inequalities, accelerating significant advancements in HNC management and transforming patient outcomes.

Keywords:

Deep Learning Head and Neck Cancer Histopathology Images Attention Mechanisms Imaging Modal‑ ities Survival Prediction Healthcare Decision‑Making

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