Hydatidiform Mole

Hydatidiform mole (HM), a gestational trophoblastic disease with malignant potential, presents diagnostic challenges due to its variable microscopic appearance. Current research focuses on developing computer-aided diagnosis (CAD) systems using deep learning, particularly semantic segmentation networks, to improve the accuracy and speed of HM hydrops lesion identification in microscopic images. These systems employ novel loss functions and training methods to enhance the performance of image segmentation, aiming to reduce misdiagnosis and improve patient outcomes. The development of robust and reliable CAD tools holds significant promise for standardizing HM diagnosis and improving the efficiency of pathology workflows.