SAC-Net: Learning with weak and noisy labels in histopathology image segmentation.
Med Image Anal
; 86: 102790, 2023 05.
Article
em En
| MEDLINE
| ID: mdl-36878159
ABSTRACT
Deep convolutional neural networks have been highly effective in segmentation tasks. However, segmentation becomes more difficult when training images include many complex instances to segment, such as the task of nuclei segmentation in histopathology images. Weakly supervised learning can reduce the need for large-scale, high-quality ground truth annotations by involving non-expert annotators or algorithms to generate supervision information for segmentation. However, there is still a significant performance gap between weakly supervised learning and fully supervised learning approaches. In this work, we propose a weakly-supervised nuclei segmentation method in a two-stage training manner that only requires annotation of the nuclear centroids. First, we generate boundary and superpixel-based masks as pseudo ground truth labels to train our SAC-Net, which is a segmentation network enhanced by a constraint network and an attention network to effectively address the problems caused by noisy labels. Then, we refine the pseudo labels at the pixel level based on Confident Learning to train the network again. Our method shows highly competitive performance of cell nuclei segmentation in histopathology images on three public datasets. Code will be available at https//github.com/RuoyuGuo/MaskGA_Net.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Núcleo Celular
Limite:
Humans
Idioma:
En
Revista:
Med Image Anal
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Austrália
País de publicação:
HOLANDA
/
HOLLAND
/
NETHERLANDS
/
NL
/
PAISES BAJOS
/
THE NETHERLANDS