Towards cross-center head and neck cancer detection: a multi-level domain alignment exploration
IntroductionDeep learning models for head and neck cancer (HNC) detection from computed tomography (CT) hold significant promise for improving early detection—a critical priority given that 5-year survival drops from 84% for localized disease to 39% for metastatic cases. However, robust cross-center deployment remains challenging because scanner vendors, acquisition protocols, reconstruction Q15 kernels, and patient populations vary across hospitals. To address this challenge, we propose MDA-Net
Record details
Published: 6 August 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 7 August 2026
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ethics.ai (6 August 2026), “Towards cross-center head and neck cancer detection: a multi-level domain alignment exploration,” evidence record 17144, https://ethics.ai/record/17144 (originally published by Frontiers in Artificial Intelligence).
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