Development and Evaluation of Interactive Machine Learning Tools for Tissue Classification in Pathological Image

Abstract

In this paper, we discuss methods for efficiently generating a large amount of labeled data to improve model accuracy in the field of machine learning used for image analysis. In the domain of image analysis, precise and detailed annotations by experts are essential, posing a significant burden on the annotators. Therefore, we develop a novel annotation tool that leverages machine learning models, exemplified by random forest, aiming to achieve both accuracy and efficiency.