Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition
This work addresses a domain-specific medical imaging challenge for pathologists, but it appears incremental as it builds on existing segmentation networks with a novel loss and training approach.
The paper tackled the problem of low accuracy in diagnosing early hydatidiform mole from hydrops lesions in medical images by developing a segmentation model with a compound loss function and stagewise training method, achieving good recognition performance across different segmentation metrics.
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.