  This paper deals with the multiple annotation problem in medical application
of cancer detection in digital images. The main assumption is that though
images are labeled by many experts, the number of images read by the same
expert is not large. Thus differing with the existing work on modeling each
expert and ground truth simultaneously, the multi annotation information is
used in a soft manner. The multiple labels from different experts are used to
estimate the probability of the findings to be marked as malignant. The
learning algorithm minimizes the Kullback Leibler (KL) divergence between the
modeled probabilities and desired ones constraining the model to be compact.
The probabilities are modeled by logit regression and multiple instance
learning concept is used by us.
  Experiments on a real-life computer aided diagnosis (CAD) problem for CXR CAD
lung cancer detection demonstrate that the proposed algorithm leads to similar
results as learning with a binary RVMMIL classifier or a mixture of binary
RVMMIL models per annotator. However, this model achieves a smaller complexity
and is more preferable in practice.
