Label-efficient Machine Learning for Diagnosing Urinary Tract Infection (UTI) in Urine Microscopy

Urinary tract infections (UTI) belong to the most common clinically relevant bacterial infections. 1 in 3 women worldwide will have at least one UTI by 24 years of age and 40 - 50% of women will experience one UTI during their lifetime with 44% experiencing recurrences. In this project, using a clinical dataset of brightfield microscopy of patients’ urine with few annotated samples,
we aim to develop a diagnostic phenotype quantification workflow using label-efficient machine learning (ML) approaches. There are several challenges to the clinical dataset at hand. Firstly, in the absence of specific labeling for phenotype-relevant objects in the micrographs ground truth is ambiguous. Secondly, obtaining manual annotations is laborious and requires highly-skilled annotators. Thirdly, the variation in scale and shape of a particular type of phenotype-relevant object is challenging for instance segmentation.