Unsupervised learning for labeling global glomerulosclerosis.
Journal:
Computers in biology and medicine
Published Date:
Aug 1, 2025
Abstract
BACKGROUND: Labeling images for supervised learning in nephropathology is highly time-consuming and dependent on domain-expertise. Unsupervised strategies have been suggested for mitigating this bottleneck. For instance, previous work suggested that clustering/grouping of glomeruli based on image features might enable a more semi-automated labeling of morphological classes or even a completely unsupervised training. However, even for the most basic separation between globally sclerosed and non-globally sclerosed glomeruli, the performance of clustering approaches has not yet been fully elucidated. The current study sought to fill this gap by extensively evaluating the accuracy and limitations of capturing these two classes via clustering.
