2021/11/19 by Dipam Goswami, Goswami, Dipam, Hari Om Aggrawal +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Quantitative Methods (q-bio.QM) #cs.CV #eess.IV #electronic engineering #information engineering #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2111.10374
7 pages, 1 image
arxiv created 2021/11/19 · arxiv updated 2021/11/23
Urinalysis is a standard diagnostic test to detect urinary system related problems. The automation of urinalysis will reduce the overall diagnostic time. Recent studies used urine microscopic datasets for designing deep learning based algorithms to classify and detect urine cells. But these datasets are not publicly available for further research. To alleviate the need for urine datsets, we prepare our urine sediment microscopic image (UMID) dataset comprising of around 3700 cell annotations and 3 categories of cells namely RBC, pus and epithelial cells. We discuss the several challenges involved in preparing the dataset and the annotations. We make the dataset publicly available.