Ranking XAI Methods for Head and Neck Cancer Outcome Prediction
arXiv cs.CV / 4/20/2026
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Key Points
- The paper addresses the challenge of predicting outcomes for head and neck cancer patients using PET/CT AI while emphasizing that interpretability is a major barrier to clinical adoption.
- It is the first study to comprehensively evaluate and rank 13 explainable AI (XAI) methods for head and neck cancer across 24 evaluation metrics.
- The evaluation covers multiple quality dimensions including faithfulness, robustness, complexity, and plausibility rather than relying on ad-hoc or purely empirical selection.
- Experiments on the multi-center HECKTOR challenge dataset show that XAI methods vary substantially, and Integrated Gradients (IG) and DeepLIFT (DL) achieve consistently high rankings across key criteria.
- The authors argue that thorough XAI assessment is critical for medical imaging deployment and that the approach can be generalized to other medical imaging tasks.
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