Molecular Classification of Breast Cancer Using Weakly Supervised Learning

The molecular classification of breast cancer is crucial for effective treatment.The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation.Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. MethodsOur approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA).Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.