Cardiac Segmentation of LGE MRI with Noisy Labels

Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - Lecture Notes in Computer Science ◽

10.1007/978-3-030-39074-7_24 ◽

2020 ◽

pp. 228-236

Author(s):

Holger Roth ◽

Wentao Zhu ◽

Dong Yang ◽

Ziyue Xu ◽

Daguang Xu

Keyword(s):

Cardiac Segmentation ◽

Noisy Labels

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Fully Automatic Cardiac Segmentation And Quantification For Pulmonary Hypertension Analysis Using Mice Cine Mr Images

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi48211.2021.9433855 ◽

2021 ◽

Author(s):

Blanca Zufiria ◽

Maialen Stephens ◽

Maria Jesus Sanchez ◽

Jesus Ruiz-Cabello ◽

Karen Lopez-Linares ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Mr Images ◽

Cardiac Segmentation ◽

Cine Mr ◽

Fully Automatic

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Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽

10.3390/chemosensors9040078 ◽

2021 ◽

Vol 9 (4) ◽

pp. 78

Author(s):

Jianhua Cao ◽

Tao Liu ◽

Jianjun Chen ◽

Tao Yang ◽

Xiuxiu Zhu ◽

...

Keyword(s):

Active Learning ◽

Gas Sensor ◽

Electronic Nose ◽

Effective Means ◽

Learning Paradigm ◽

Class Label ◽

Learning Framework ◽

Sensor Drift ◽

Drift Compensation ◽

Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

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