.png){kind=icon}
返回
AL-Net: Attention Learning Network Based on Multi-Task Learning for Cervical Nucleus Segmentation
.png){kind=icon}
PRE
AI
DOI：10.1109/JBHI.2021.3136568.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and adherent or overlapping nuclei in nucleus images. To overcome the limitation of current methods, we propose a multi-task network based on U-Net for cervical nucleus segmentation. This network consists of a primary task and an auxiliary task. The primary task is employed to predict nuclei regions. The auxiliary task, which predicts the boundaries of nuclei, is designed to improve the feature extraction of the main task. Furthermore, a context encoding layer is added behind each encoding layer of the U-Net. The output of each context encoding layer is processed by an attention learning module and then fused with the features of the decoding layer. In addition, a codec block is used in the attention learning module to obtain saliency-based attention and focused attention simultaneously. Experiment results show that the proposed network performs better than the state-of-the-art methods on the 2014 ISBI dataset, BNS, MoNuSeg, and our nucluesSeg dataset.
Keyword:
Image segmentation
Task analysis
Feature extraction
Encoding
Multitasking
Shape
Convolution
Nucleus segmentation
multi-task network
attention learning
context encoding
shape loss
期刊
IF:
6.8
论文数:
4.5K
被引数:
2.0W
机构
引用论文
分享收藏Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology imagesHover-Net: 多组织组织学图像中细胞核的同时分割和分类.png)
MEDICAL IMAGE ANALYSIS
OAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏DCAN: Deep contour-aware networks for object instance segmentation from histology imagesDban: 用于从组织学图像中分割对象实例的深度轮廓感知网络MEDICAL IMAGE ANALYSIS
MEDICAL IMAGE ANALYSISIF11.8
PREAI
分享收藏分享收藏
查看更多内容