Project DetailsThe goal of the work is to analyze histopathology images of the thyroid gland using deep learning algorithms. The algorithm is designed to reduce the workload of pathologists in identifying regions of interest in the biopsys samples in order to minimize human errors in the identification process. We chose to perform the segmentation using the U-Net network. Following the classification, we performed feature detection to classify the subtype of thyroid cancer using the StarDist network. We conducted our tests using datasets from two hospitals, NKI in the Netherlands and VGH in Japan, for the U-Net algorithm, and the MoNuSeg dataset for the StarDist network. According to the IoU metric, we achieved an 80% correct identification rate on the U-Net network for the test set, and a correct identification rate of 82% on the StarDist network.
