Do we need large annotated training data for detection applications in biomedical imaging? A case study in renal glomeruli detection

Approaches for detecting regions of interest in biomedical image data mostly assume that a large amount of annotated training data is available. Certainly, for unchanging problem definitions, the acquisition of large annotated data is time consuming, yet feasible. However, distinct practical problems with large training corpi arise if variability due to different imaging conditions or inter-personal variations lead to significant changes in the image representation. To circumvent these issues, we investigate a classifier learning scenario which requires a small amount of positive annotation data only. Contrarily to previous approaches which focus on methodologies to explicitly or implicitly deal with specific classification scenarios (such as one-class classification), we show that existing supervised classification models can handle a changed setting effectively without any specific modifications.