  Two of the most common tasks in medical imaging are classification and
segmentation. Either task requires labeled data annotated by experts, which is
scarce and expensive to collect. Annotating data for segmentation is generally
considered to be more laborious as the annotator has to draw around the
boundaries of regions of interest, as opposed to assigning image patches a
class label. Furthermore, in tasks such as breast cancer histopathology, any
realistic clinical application often includes working with whole slide images,
whereas most publicly available training data are in the form of image patches,
which are given a class label. We propose an architecture that can alleviate
the requirements for segmentation-level ground truth by making use of
image-level labels to reduce the amount of time spent on data curation. In
addition, this architecture can help unlock the potential of previously
acquired image-level datasets on segmentation tasks by annotating a small
number of regions of interest. In our experiments, we show using only one
segmentation-level annotation per class, we can achieve performance comparable
to a fully annotated dataset.
