  We consider machine-learning-based thyroid-malignancy prediction from
cytopathology whole-slide images (WSI). Multiple instance learning (MIL)
approaches, typically used for the analysis of WSIs, divide the image (bag)
into patches (instances), which are used to predict a single bag-level label.
These approaches perform poorly in cytopathology slides due to a unique bag
structure: sparsely located informative instances with varying characteristics
of abnormality. We address these challenges by considering multiple types of
labels: bag-level malignancy and ordered diagnostic scores, as well as
instance-level informativeness and abnormality labels. We study their
contribution beyond the MIL setting by proposing a maximum likelihood
estimation (MLE) framework, from which we derive a two-stage
deep-learning-based algorithm. The algorithm identifies informative instances
and assigns them local malignancy scores that are incorporated into a global
malignancy prediction. We derive a lower bound of the MLE, leading to an
improved training strategy based on weak supervision, that we motivate through
statistical analysis. The lower bound further allows us to extend the proposed
algorithm to simultaneously predict multiple bag and instance-level labels from
a single output of a neural network. Experimental results demonstrate that the
proposed algorithm provides competitive performance compared to several
competing methods, achieves (expert) human-level performance, and allows
augmentation of human decisions.
