Breast Tumor Cellularity Assessment using Deep Neural Networks Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in postsurgical tissues is used to analyze a tumoru0027s response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patientu0027s survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learningbased approaches for automatic assessment of tumor cellularity from posttreated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohenu0027s kappa coefficient of 0.70 (vs. 0.42 previously known in literature) and the intraclass correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learningbased methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment.

Joint region and nucleus segmentation for characterization of tumor infiltrating lymphocytes in breast cancer Histologic assessment of stromal tumor infiltrating lymphocytes (sTIL) as a surrogate of the host immune response has been shown to be prognostic and potentially chemopredictive in triplenegative and HER2positive breast cancers. The current practice of manual assessment is prone to intra and interobserver variability. Furthermore, the interplay of sTILs, tumor cells, other microenvironment mediators, their spatial relationships, quantity, and other imagebased features have yet to be determined exhaustively and systemically. Towards analysis of these aspects, we developed a deep learning based method for joint regionlevel and nucleuslevel segmentation and classification of breast cancer Hu0026E tissue whole slide images. Our proposed method simultaneously identifies tumor, fibroblast, and lymphocyte nuclei, along with key histologic region compartments including tumor and stroma. We also show how the resultant segmentation masks can be combined with seeding approaches to yield accurate nucleus classifications. Furthermore, we outline a simple workflow for calibrating computational scores to human scores for consistency. The pipeline identifies key compartments with high accuracy (Dice overall: 0.78, tumor: 0.83, and fibroblasts: 0.77). ROC AUC for nucleus classification is high at 0.89 (microaverage), 0.89 (lymphocytes), 0.90 (tumor), and 0.78 (fibroblasts). Spearman correlation between computational sTIL and pathologist consensus is high (R0.73, pu003c0.001) and is higher than interpathologist correlation (R0.66, pu003c0.001). Both manual and computational sTIL scores successfully stratify patients by clinical progression outcomes.