Ricerc@Sapienza

Urine cytology samples constitute a significant percentage of daily non-gynecologic cases in any cytopathology laboratory, and one of the most difficult specimens that pathologists encounter. The most relevant problems are the low sensitivity to detect low-grade, non-invasive lesions, absence of standardized diagnostic criteria, and interobserver variability. To meet this need, the Paris System 2016 classification proposed a standardized reporting system, including specific diagnostic categories and cytopathological criteria for the differential diagnosis of atypical cells. However, this method is based on a number of objective and subjective criteria (e.g., cell atypia, irregularity, and hyperchromasia), making detection an extremely time-consuming operation for clinicians. In this project, we plan to develop an automatic support method for detecting urothelial cancer cells from image data, based on recent progresses in the field of deep neural networks (DNNs). DNNs have become a de-facto standard in the computer vision field, and they have shown promising results in other medical scenarios, such as segmentation of chest radiographies. At the same time, out-of-the-box DNNs need large amounts of (expertly curated) data, and they tend to be poorly interpretable and overconfident in their predictions. Broadly, the aims of this project are (i) collecting a large database of urine cytology images classified according to the Paris System; (ii) implementing a state-of-the-art deep network to automatically classify, with high accuracy, new samples; (iii) carefully study the robustness, calibration, and interpretability of the network; and (iv) design a test scenario to gather data on the interaction between the trained system and real clinicians. The system offers the possibility of exploiting recent artificial intelligence techniques in urothelial cancer cell detection, significantly increasing sensitivity, accuracy and diagnostic reproducibility.