  Pathological morphology diagnosis is the standard diagnosis method of
hydatidiform mole. As a disease with malignant potential, the hydatidiform mole
section of hydrops lesions is an important basis for diagnosis. Due to
incomplete lesion development, early hydatidiform mole is difficult to
distinguish, resulting in a low accuracy of clinical diagnosis. As a remarkable
machine learning technology, image semantic segmentation networks have been
used in many medical image recognition tasks. We developed a hydatidiform mole
hydrops lesion segmentation model based on a novel loss function and training
method. The model consists of different networks that segment the section image
at the pixel and lesion levels. Our compound loss function assign weights to
the segmentation results of the two levels to calculate the loss. We then
propose a stagewise training method to combine the advantages of various loss
functions at different levels. We evaluate our method on a hydatidiform mole
hydrops dataset. Experiments show that the proposed model with our loss
function and training method has good recognition performance under different
segmentation metrics.
