A dual-modal CNN system fuses CT and H&E features with clinical metadata to classify lung cancer subtypes at 0.87 accuracy and 0.97 AUROC, using multiple XAI methods for interpretability.
Proceedings of the 36th International Conference on Machine Learning (ICML) (2019)
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Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
A dual-modal CNN system fuses CT and H&E features with clinical metadata to classify lung cancer subtypes at 0.87 accuracy and 0.97 AUROC, using multiple XAI methods for interpretability.