Paper ID: 2305.04332

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.

Submitted: May 7, 2023