Patch relevance estimation and multilabel augmentation for weakly supervised histopathology image classification

The proposed architecture effectively models the complex relationship between image-level labels and patch-level content in multiclass histopathological image analysis. Combined with the image-level multilabel augmentation strategy, it improves learning under label uncertainty. These contributions hold potential for more accurate and scalable diagnostic support systems in digital pathology.