HistoVault: Building a Comprehensive Cancer Histopathology Image Dataset Using a Low Cost, Low Resolution (LCLR) Approach

Converting pathological images into mineable datasets based on AI algorithms and linking these extracted and quantified pathological features to clinically related indicators is called Pathomics (1). The project aimed to create a comprehensive dataset of cancer histopathology images, covering various cancer types and subtypes, to train AI models for accurate cancer detection using standard digitization techniques (2). It focused on standardizing slide collection and labeling to ensure high-quality digital images that meet clinical and research standards. Additionally, it sought to establish HistoVault as a reliable repository, advancing AI-driven diagnostics and cancer research (3).