Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (HE P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.