  Object localisation is typically considered in the context of regular images,
for instance depicting objects like people or cars. In these images there is
typically a relatively small number of instances per image per class, which
usually is manageable to annotate. However, outside the realm of regular images
we are often confronted with a different situation. In computational pathology
digitised tissue sections are extremely large images, whose dimensions quickly
exceed 250'000x250'000 pixels, where relevant objects, such as tumour cells or
lymphocytes can quickly number in the millions. Annotating them all is
practically impossible and annotating sparsely a few, out of many more, is the
only possibility. Unfortunately, learning from sparse annotations, or
sparse-shot learning, clashes with standard supervised learning because what is
not annotated is treated as a negative. However, assigning negative labels to
what are true positives leads to confusion in the gradients and biased
learning. To this end, we present exclusive cross entropy, which slows down the
biased learning by examining the second-order loss derivatives in order to drop
the loss terms corresponding to likely biased terms. Experiments on nine
datasets and two different localisation tasks, detection with YOLLO and
segmentation with Unet, show that we obtain considerable improvements compared
to cross entropy or focal loss, while often reaching the best possible
performance for the model with only 10-40 of annotations.
