Fig. 1: Overview of data annotation, the two-stage deep learning system (DLS), and interpretability techniques.
From: Determining breast cancer biomarker status and associated morphological features using deep learning
Annotation overview: paired H&E and IHC images were used to develop regional biomarker annotations (see Supplementary Fig. 4). Case-level biomarker status labels were obtained from available pathology reports. DLS overview: a model based on the Inception-v3 architecture was developed for each biomarker. Model interpretability: saliency maps and unsupervised clustering provided an exploratory approach to interpretability, while concept activation vector analysis provided hypothesis-driven analysis of features associated with biomarker predictions. H&E Hematoxylin and Eosin, ER Estrogen Receptor, PR Progesterone Receptor, HER2 human epidermal growth factor receptor 2.
