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

Weakly supervised learning (WSL) is widely used for histopathological image analysis by modeling images as sets of fixed-size patches and utilizing image-level diagnoses as weak labels. However, in multiclass classification scenarios, patches corresponding to a wide spectrum of diagnostic categories can co-exist in a single image, complicating the learning process. We aim to address label uncertainty in such multiclass settings.

We propose a two-branch architecture and a complementary training strategy to improve patch-based WSL. One branch estimates patch-level class likelihoods, whereas the other predicts per-class patch relevance weights. These outputs are combined into image-level class predictions via a relevance-weighted sum of per-patch class likelihoods. To further improve performance, we introduce a multilabel augmentation strategy that forms new training samples by combining patch sets and labels from pairs of images, resulting in multilabel samples that enrich the training set by increasing the chance of having more patches that are relevant to the augmented label sets.

We evaluate our method on two challenging multiclass breast histopathology datasets for region of interest classification. The proposed architecture and training strategy outperform conventional weakly supervised methods, demonstrating improved classification accuracy and robustness, particularly in underrepresented classes.

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.