Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis
This work addresses fast tumor diagnosis for veterinary medicine, but it is incremental as it applies existing deep learning methods to a new domain-specific dataset.
The paper tackles the problem of segmenting cytological images in veterinary medicine to diagnose neoplastic tumors, achieving high average precision and recall scores of 0.94 and 0.8 for three tumor types.
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.