Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

ABSTRACT: Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95%

SUBMITTER: Lee J 

PROVIDER: S-EPMC8941143 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature