Efficient annotation bootstrapping for cell identification in follicular lymphoma.
Journal:
Computer methods and programs in biomedicine
Published Date:
Mar 27, 2025
Abstract
BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an opportunity to apply computer vision methods. However, acquiring a substantial number of annotations for developing deep learning algorithms remains a bottleneck. The annotation process is inherently biased due to various constraints, including labor shortages, high costs, time inefficiencies, and a strongly imbalanced distribution of labels. This study explores available solutions for reducing the costs of annotation bootstrapping in the challenging task of follicular lymphoma diagnosis.