Combating Medical Label Noise through more precise partition-correction and progressive hard-enhanced learning.
Journal:
Computer methods and programs in biomedicine
PMID:
40168942
Abstract
BACKGROUND AND OBJECTIVE: Computer-aided diagnosis systems based on deep neural networks heavily rely on datasets with high-quality labels. However, manual annotation for lesion diagnosis relies on image features, often requiring professional experience and complex image analysis process. This inevitably introduces noisy labels, which can misguide the training of classification models. Our goal is to design an effective method to address the challenges posed by label noise in medical images.