The volume-corrected mitotic index (M/V-Index) was shown to provide
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 using a
deep learning pipeline solely trained with an annotation-free,
immunohistochemistry-based approach, provides accurate estimations of
epithelial segmentation in canine breast 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.

通过使用基于免疫组化的无注释深度学习流程，我们对犬乳腺癌的上皮分割进行了准确估计，并将我们的自动框架与手动注释的体积校正有丝分裂指数进行了比较，结果表明深度学习的流程具有专家级的性能，同时具备时间效率和重复性。