Multi-instance multi-label (MIML) learning is a challenging problem in many
aspects. Such learning approaches might be useful for many medical diagnosis
applications including breast cancer detection and classification. In this
study subset of digiPATH dataset (whole slide digital breast cancer
histopathology images) are used for training and evaluation of six
state-of-the-art MIML methods.
At the end, performance comparison of these approaches are given by means of
effective evaluation metrics. It is shown that MIML-kNN achieve the best
performance that is %65.3 average precision, where most of other methods attain
acceptable results as well