Active Label Selection for Annotation Costs in Pathology Slide Repositories: Mixed Evaluation

Abstract

The digitization of histopathology has led to the creation of massive pathology slide repositories, offering unprecedented opportunities for training robust deep learning models in computational pathology. However, annotating these gigapixel whole slide images is a highly specialized, time-consuming, and expensive process, creating a significant bottleneck in supervised machine learning workflows. This paper presents a comprehensive, mixed evaluation of active label selection strategies and their direct impact on annotation costs within large-scale pathology repositories. By systematically analyzing the intersection of uncertainty-based and diversity-based sampling methodologies, we aim to quantify the trade-off between model performance and human expert labor. The study provides a detailed methodological framework to measure the cognitive and financial load placed on pathologists when utilizing conventional uniform sampling versus intelligent active label selection. The findings indicate that implementing a hybrid active learning approach significantly reduces the sheer volume of required annotations while maintaining or exceeding diagnostic accuracy thresholds. Ultimately, this research offers a practical pathway for healthcare institutions to optimize their computational resource allocation and annotation budgets, thereby accelerating the deployment of clinical-grade artificial intelligence systems in diagnostic pathology.