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Found 3 papers, 1 papers with code
Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition
We developed a hydatidiform mole hydrops lesion segmentation model based on a novel loss function and training method.
A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View
We constructed a deep-learning-based CAD system to identify HM hydrops lesions in the microscopic view in real-time.
Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
In the ten-fold cross-validation process, the CADx approach, HIENet, achieved a 76. 91 $\pm$ 1. 17% (mean $\pm$ s. d.) classification accuracy for four classes of endometrial tissue, namely normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma.
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