  As a disease with malignant potential, hydatidiform mole (HM) is one of the
most common gestational trophoblastic diseases. For pathologists, the HM
section of hydrops lesions is an important basis for diagnosis. In pathology
departments, the diverse microscopic manifestations of HM lesions and the
limited view under the microscope mean that physicians with extensive
diagnostic experience are required to prevent missed diagnosis and
misdiagnosis. Feature extraction can significantly improve the accuracy and
speed of the diagnostic process. As a remarkable diagnosis assisting
technology, computer-aided diagnosis (CAD) has been widely used in clinical
practice. We constructed a deep-learning-based CAD system to identify HM
hydrops lesions in the microscopic view in real-time. The system consists of
three modules; the image mosaic module and edge extension module process the
image to improve the outcome of the hydrops lesion recognition module, which
adopts a semantic segmentation network, our novel compound loss function, and a
stepwise training function in order to achieve the best performance in
identifying hydrops lesions. We evaluated our system using an HM hydrops
dataset. Experiments show that our system is able to respond in real-time and
correctly display the entire microscopic view with accurately labeled HM
hydrops lesions.
