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As a disease with malignant potential, hydatidiform mole (HM) is one of themost common gestational trophoblastic diseases. For pathologists, the HMsection of hydrops lesions is an important basis for diagnosis. In pathologydepartments, the diverse microscopic manifestations of HM lesions and thelimited view under the microscope mean that physicians with extensivediagnostic experience are required to prevent missed diagnosis andmisdiagnosis. Feature extraction can significantly improve the accuracy andspeed of the diagnostic process. As a remarkable diagnosis assistingtechnology, computer-aided diagnosis (CAD) has been widely used in clinicalpractice. We constructed a deep-learning-based CAD system to identify HMhydrops lesions in the microscopic view in real-time. The system consists ofthree modules; the image mosaic module and edge extension module process theimage to improve the outcome of the hydrops lesion recognition module, whichadopts a semantic segmentation network, our novel compound loss function, and astepwise training function in order to achieve the best performance inidentifying hydrops lesions. We evaluated our system using an HM hydropsdataset. Experiments show that our system is able to respond in real-time andcorrectly display the entire microscopic view with accurately labeled HMhydrops lesions.

A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View