Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition

TL;DR

This paper presents a novel segmentation model with a compound loss function and stagewise training for improved recognition of hydatidiform mole hydrops lesions in medical images, addressing early diagnosis challenges.

Contribution

The paper introduces a new loss function and training approach for segmentation networks, specifically tailored for hydatidiform mole lesion recognition in medical imaging.

Findings

Achieved high segmentation accuracy on hydatidiform mole dataset.

Demonstrated the effectiveness of the compound loss function and stagewise training.

Outperformed baseline models in lesion recognition metrics.

Abstract

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.