Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks

SIMPLE SUMMARY: The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. Deep learning algorithms, based on convolutional neural networks, have been successfully applied to the classification and segmentation of medical images. The aim was to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs). Total 39,000 ECCs (26,880 for training, 11,520 for testing and 600 malignant for verification) patches were obtained by the segmentation network. The training set reached 100% accuracy, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. Therefore, an artificial intelligence system was successfully built to classify malignant and benign ECCs for reducing pathologists’ workload, providing decision-making assistance and promoting the development of endometrial cancer screening. ABSTRACT: Objectives: The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs). Methods: We used Li Brush to acquire endometrial cells from patients. Liquid-based cytology technology was used to provide slides. The slides were scanned and divided into malignant and benign groups. We proposed two (a U-net segmentation and a DenseNet classification) networks to identify images. Another four classification networks were used for comparison tests. Results: A total of 113 (42 malignant and 71 benign) endometrial samples were collected, and a dataset containing 15,913 images was constructed. A total of 39,000 ECCs patches were obtained by the segmentation network. Then, 26,880 and 11,520 patches were used for training and testing, respectively. On the premise that the training set reached 100%, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. The remaining 600 malignant patches were used for verification. Conclusions: An artificial intelligence system was successfully built to classify malignant and benign ECCs.