  Classification models for the automatic detection of abnormalities on
histological samples do exists, with an active debate on the cost associated
with false negative diagnosis (underdiagnosis) and false positive diagnosis
(overdiagnosis). Current models tend to underdiagnose, failing to recognize a
potentially fatal disease.
  The objective of this study is to investigate the possibility of
automatically identifying abnormalities in tissue samples through the use of an
ensemble model on data generated by histological examination and to minimize
the number of false negative cases.
