Classifying nuclei in soft oral tissue slides

Sammanfattning: A big focus of pathology is the analysis of tissues, cells, and body fluid samples. In recent years, with the advent of high resolution scanners, we have seen an increase in the use of artificial intelligence in pathology. In this work we present a new dataset (KI dataset), compiled from slides of soft oral tissue and the present nuclei labeled by pathologists at the Department of Dental Medicine at Karolinska Institutet. We test the performance of two fully trained neural networks along with one fine-tuned deep model on the KI dataset, aimed at classifying nuclei from the slides into one of three classes. The first fully trained network is a shallow CNN, with the second fully trained network being a slightly deeper version of the first one. Both of these networks have previously been used for the task of nuclei classification, but on different datasets. We also test the performance of a fine-tuned VGG16 model, where we train the last layer of a model pre-trained on the widely used ImageNet dataset. The fine-tuned deep neural network (VGG16) produces promising results and we show that the fully trained models perform better on the KI dataset, compared to their results on a dataset of colon cancer slides, leading us to believe that the dataset is good and can be used in further research.