A Multi-resolution Coarse-to-Fine Segmentation Framework with Active Learning in 3D Brain MRI

2019 ◽  
pp. 285-298
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
Xinbo Gao
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Segmentation Framework ◽  
Coarse To Fine

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method

scholarly journals Cost-Effective Active Learning for Hierarchical Multi-Label Classification

2018 ◽  
Author(s):  
Yi-Fan Yan ◽  
Sheng-Jun Huang
Keyword(s):  
Active Learning ◽  
Subset Selection ◽  
Cost Effective ◽  
Selection Method ◽  
Learning Approach ◽  
Potential Contribution ◽  
Annotation Costs ◽  
Model Training ◽  
Coarse To Fine ◽  
Different Levels

Active learning reduces the labeling cost by actively querying labels for the most valuable data. It is particularly important for multi-label learning, where the annotation cost is rather high because each instance may have multiple labels simultaneously. In many multi-label tasks, the labels are organized into hierarchies from coarse to fine. The labels at different levels of the hierarchy contribute differently to the model training, and also have diverse annotation costs. In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries. By incorporating the potential contribution of ancestor and descendant labels, a novel criterion is proposed to estimate the informativeness of each candidate query. Further, a subset selection method is introduced to perform active batch selection by balancing the informativeness and cost of each instance-label pair. Experimental results validate the effectiveness of both the proposed criterion and the selection method.

scholarly journals Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction

2019 ◽  
Author(s):  
Yue Zhang ◽  
Jiong Wu ◽  
Yifan Chen ◽  
Ed X. Wu ◽  
Xiaoying Tang
Keyword(s):  
Abnormal Detection ◽  
Kidney Segmentation ◽  
Segmentation Framework ◽  
Coarse To Fine

Active Learning Through Use of Learning and Study Strategies

1990 ◽  
Vol 35 (5) ◽  
pp. 471-472
Author(s):  
Herbert J. Klausmeier
Keyword(s):  
Active Learning ◽  
Study Strategies ◽  
Learning And Study Strategies

A randomized controlled trial of an online, modular, active learning training program for behavioral activation for depression.

2017 ◽  
Vol 85 (8) ◽  
pp. 814-825 ◽  
Author(s):  
Ajeng J. Puspitasari ◽  
Jonathan W. Kanter ◽  
Andrew M. Busch ◽  
Rachel Leonard ◽  
Shira Dunsiger ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Active Learning ◽  
Training Program ◽  
Behavioral Activation ◽  
Controlled Trial ◽  
Randomized Controlled
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