Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry
摘要
The volume-corrected mitotic index (M/V-Index) has demonstrated prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that the use of a deep learning pipeline solely trained with an annotation-free, immunohistochemistrybased approach, provides accurate estimates of epithelial segmentation in canine mammary carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility.