  We consider preoperative prediction of thyroid cancer based on
ultra-high-resolution whole-slide cytopathology images. Inspired by how human
experts perform diagnosis, our approach first identifies and classifies
diagnostic image regions containing informative thyroid cells, which only
comprise a tiny fraction of the entire image. These local estimates are then
aggregated into a single prediction of thyroid malignancy. Several unique
characteristics of thyroid cytopathology guide our deep-learning-based
approach. While our method is closely related to multiple-instance learning, it
deviates from these methods by using a supervised procedure to extract
diagnostically relevant regions. Moreover, we propose to simultaneously predict
thyroid malignancy, as well as a diagnostic score assigned by a human expert,
which further allows us to devise an improved training strategy. Experimental
results show that the proposed algorithm achieves performance comparable to
human experts, and demonstrate the potential of using the algorithm for
screening and as an assistive tool for the improved diagnosis of indeterminate
cases.
