Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study
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New Calibratable Disambiguation Loss Improves AI Classifier Reliability
Researchers have introduced a new method called Calibratable Disambiguation Loss (CDL) to improve the reliability of classifiers in Multi-Instance Partial-Label Learning (MIPL) tasks. This plug-and-play loss function en…
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New Paper Reinterprets Image Classifiers as Multi-Instance Learners
A new paper proposes a re-evaluation of global average pooling (GAP) in image classifiers, suggesting that these models can be interpreted as multi-instance learners. The research indicates that even when an image-level…