  For the task of concurrently detecting and categorizing objects, the medical
imaging community commonly adopts methods developed on natural images. Current
state-of-the-art object detectors are comprised of two stages: the first stage
generates region proposals, the second stage subsequently categorizes them.
Unlike in natural images, however, for anatomical structures of interest such
as tumors, the appearance in the image (e.g., scale or intensity) links to a
malignancy grade that lies on a continuous ordinal scale. While classification
models discard this ordinal relation between grades by discretizing the
continuous scale to an unordered bag of categories, regression models are
trained with distance metrics, which preserve the relation. This advantage
becomes all the more important in the setting of label confusions on ambiguous
data sets, which is the usual case with medical images. To this end, we propose
Reg R-CNN, which replaces the second-stage classification model of a current
object detector with a regression model. We show the superiority of our
approach on a public data set with 1026 patients and a series of toy
experiments. Code will be available at github.com/MIC-DKFZ/RegRCNN.
