# A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View Source: [A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:07:48.255Z Authors: Chengze Zhu, Pingge Hu, Xianxu Zeng, Xingtong Wang, Zehua Ji, Li Shi Published: 2022-04-11T08:51:16.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()