Segmentation of the veterinary cytological images for fast neoplastic
  tumors diagnosis
Jakub Grzeszczyk Michał Karwatowski Daria Łukasik Maciej Wielgosz Paweł Russek Szymon Mazurek Jakub Caputa Rafał Frączek Anna Śmiech Ernest Jamro Sebastian Koryciak Agnieszka Dąbrowska-Boruch Marcin Pietroń Kazimierz Wiatr
This paper shows the machine learning system which performs instance
segmentation of cytological images in veterinary medicine. Eleven cell types
were used directly and indirectly in the experiments, including damaged and
unrecognized categories. The deep learning models employed in the system
achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8
respectively, for the selected three types of tumors. This variety of label
types allowed us to draw a meaningful conclusion that there are relatively few
mistakes for tumor cell types. Additionally, the model learned tumor cell
features well enough to avoid misclassification mistakes of one tumor type into
another. The experiments also revealed that the quality of the results improves
with the dataset size (excluding the damaged cells). It is worth noting that
all the experiments were done using a custom dedicated dataset provided by the
cooperating vet doctors.