.png){kind=icon}
返回
S3R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites
.png){kind=icon}
PRE
AI
DOI：10.1109/TMI.2023.3260974.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ((SR)-R-3) method for explainable cross-site continual segmentation to maintain both shape and semantic knowledge of previously learned sites. Specifically, (SR)-R-3 method adopts a selective regularization scheme to penalize changes of parameters with high Joint Shape and Semantics-based Importance (JSSI) weights, which are estimated based on the parameter sensitivity to shape properties and reliable semantics of the segmentation object. This helps to prevent the related shape and semantic knowledge from being forgotten. Moreover, we propose an Importance Activation Mapping (IAM) method for memory interpretation, which indicates the spatial support for important parameters to visualize the memorized content. We have extensively evaluated our method on prostate segmentation and optic cup and disc segmentation tasks. Our method outperforms other comparison methods in reducing model forgetting and increasing explainability. Our code is available at https://github.com/jingyzhang/S3R.
Keyword:
Continual segmentation
selective regularization
memory interpretation
multi-site learning
期刊
IF:
9.8
论文数:
6.2K
被引数:
3.7W
机构
引用论文
分享收藏Electrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THV.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
分享收藏Short-term effects of pollen species on hospital admissions in the city of Madrid in terms of specific causes and ageAerobiologia
AerobiologiaIF0
PREAI
分享收藏
查看更多内容