Name
Title | ||
|---|---|---|
M-SEAM-NAM: Multi-instance Self-supervised Equivalent Attention Mechanism with Neighborhood Affinity Module for Double Weakly Supervised Segmentation of COVID-19 |
Abstract | ||
|---|---|---|
The Coronavirus Disease 2019 (COVID-19) pandemic has swept the whole world since 2019. Chest computed tomography (CT) plays an important role in clinical diagnosis, management and progression monitoring of COVID-19 patients. In order to decrease the cost of manual segmentation, weakly supervised segmentation methods, such as class activation maps (CAM) based methods, have been applied to achieve COVID-19-related lesion segmentation. Such methods could be used to localize the lesion preliminarily, but it is not precise enough to segment the lesion. In this paper, we propose a double weakly supervised segmentation method to achieve the segmentation of COVID-19 lesions on CT scans. A self-supervised equivalent attention mechanism with neighborhood affinity module is proposed for accurate segmentation. Multi-instance learning is adopted for training using annotations weaker than image-level. A simple pre-training process is also proved to be effective. We achieve a higher average Dice compared to Unet (0.782 vs 0.601) on COVID-19 lesion segmentation tasks. Codes in this paper will be available at https://github.com/TangWen920812/M-SEAM-NAM. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87234-2_25 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII |
Keywords | DocType | Volume |
Weakly supervised segmentation, Multi-instance learning, COVID-19 | Conference | 12907 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Tang | 1 | 0 | 1.01 |
| Han Kang | 2 | 0 | 1.01 |
| Ying Cao | 3 | 57 | 9.01 |
| Pengxin Yu | 4 | 0 | 1.01 |
| Hu Han | 5 | 752 | 40.02 |
| Rongguo Zhang | 6 | 0 | 1.01 |
| Kuan Chen | 7 | 1 | 1.35 |