CRU-Net: A Deep Learning Network for Semantic Segmentation of Pathological Tissue Slices

Li, Yang

doi:10.1109/aiid51893.2021.9456469

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image segmentation network based on deep learning has strong learning ability and greatly improves the accuracy of lesion recognition in pathological images. Segmentation networks commonly used for assisiting diagnosis include CNN [31]- [33], U-Net [15][29] [30], DeepLab Series [34], etc. On the basis of the above network, combined with the characteristics of pathological image data set, a more targeted network model is proposed.…”

Section: Pathological Section Image Segmentationmentioning

confidence: 99%

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

Zhu¹,

Hu²,

Zeng³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract