DGDI: A Dataset for Detecting Glomeruli on Renal Direct Immunofluorescence

Abstract

With the growing popularity of whole slide scanners, there is a high demand to develop computer aided diagnostic techniques for this new digitized pathology data. The ability to extract effective information from digital slides, which serve as fundamental representations of the prognostic data patterns or structures, provides promising opportunities to improve the accuracy of automatic disease diagnosis. The recent advances in computer vision have shown that Convolutional Neural Networks (CNNs) can be used to analyze digitized pathology images providing more consistent and objective information to the pathologists. In this paper, to advance the progress in developing computer aided diagnosis systems for renal direct immunofluorescence test, we introduce a new benchmark dataset for Detecting Glomeruli on renal Direct Immunofluorescence (DGDI). To build the baselines, we investigate various CNN-based detectors on DGDI. Experiments demonstrate that DGDI well represents the challenges of renal direct immunofluorescence image analysis and encourages the progress in developing new approaches for understanding renal disease.