An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.
The proposed IMIL-Net provides a high-accuracy, interpretable, and clinically-aligned diagnostic solution by analyzing complete patient examinations and providing a statistically validated decision-making process, and represents a trustworthy tool for integration into the clinical otolaryngology workflow.