  Molecular subtypes of breast cancer are important references to personalized
clinical treatment. For cost and labor savings, only one of the patient's
paraffin blocks is usually selected for subsequent immunohistochemistry (IHC)
to obtain molecular subtypes. Inevitable sampling error is risky due to tumor
heterogeneity and could result in a delay in treatment. Molecular subtype
prediction from conventional H&E pathological whole slide images (WSI) using AI
method is useful and critical to assist pathologists pre-screen proper paraffin
block for IHC. It's a challenging task since only WSI level labels of molecular
subtypes can be obtained from IHC. Gigapixel WSIs are divided into a huge
number of patches to be computationally feasible for deep learning. While with
coarse slide-level labels, patch-based methods may suffer from abundant noise
patches, such as folds, overstained regions, or non-tumor tissues. A weakly
supervised learning framework based on discriminative patch selecting and
multi-instance learning was proposed for breast cancer molecular subtype
prediction from H&E WSIs. Firstly, co-teaching strategy was adopted to learn
molecular subtype representations and filter out noise patches. Then, a
balanced sampling strategy was used to handle the imbalance in subtypes in the
dataset. In addition, a noise patch filtering algorithm that used local outlier
factor based on cluster centers was proposed to further select discriminative
patches. Finally, a loss function integrating patch with slide constraint
information was used to finetune MIL framework on obtained discriminative
patches and further improve the performance of molecular subtyping. The
experimental results confirmed the effectiveness of the proposed method and our
models outperformed even senior pathologists, with potential to assist
pathologists to pre-screen paraffin blocks for IHC in clinic.
