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.

翻译：本文展示了一种在兽医细胞学图像中执行实例分割的机器学习系统。实验中直接或间接使用了十一种细胞类型，包括受损和无法识别的类别。该系统采用的深度学习模型在选定的三种肿瘤类型中取得了较高的平均精度和召回率指标，分别达到0.94和0.8。多种标签类型使得我们能够得出有意义的结论：肿瘤细胞类型的错误识别相对较少。此外，模型对肿瘤细胞特征的学习足够充分，有效避免了将一种肿瘤类型误分类为另一种的情况。实验还表明，结果质量随数据集规模扩大而提升（不包括受损细胞）。值得注意的是，所有实验均使用合作兽医提供的专属定制数据集完成。