Exploring Pathologist Knowledge for Automatic Assessment of Breast Cancer Metastases in Whole-slide Image

Automatic assessment of breast cancer metastases plays an important role to help pathologist reduce the time-consuming work in histopathological whole-slide image diagnosis. From the utilization of knowledge point of view, the low-magnification level and high-magnification level are carefully checked by the pathologists for tumor pattern and cell tumor characteristic. In this paper, we propose a novel automatic patient-level tumor segmentation and classification method, which makes full use of the diagnosis knowledge clues from pathologists. For tumor segmentation, a multi-level view DeepLabV3+ (MLV-DeepLabV3+) is designed to explore the distinguishing features of cell characteristics between tumor and normal tissue. Furthermore, the expert segmentation models are selected and integrated by Pareto-front optimization to imitate the expert consultation to get perfect diagnosis. For wholeslide classification, multi-level magnifications are adaptive checked to focus on the effective features in different magnification. The experimental results demonstrate that our pathologist knowledge-based automatic assessment of whileslide image is effective and robust on the public benchmark dataset.