Modeling Annotator Variation and Annotator Preference for Multiple Annotations Medical Image Segmentation-Reference-Cited by-同舟云学术

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Modeling Annotator Variation and Annotator Preference for Multiple Annotations Medical Image Segmentation

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Guo Xutao¹,Lu Shang¹,Yang Yanwu¹,Shi Pengcheng¹,Ye Chenfei²,Xiang Yang³,Ma Ting¹

Affiliation:

1. Harbin Institute of Technology (Shenzhen),Electronic & Informatin Engineering School,Shenzhen,China

2. International Research Institute for Artifcial Intelligence, Harbin Institute of Technology (Shenzhen),Shenzhen,China

3. Peng Cheng Laboratory,Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995619.pdf?arnumber=9995619

Reference40 articles.

1. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

2. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

3. On the Importance of Label Quality for Semantic Segmentation

4. U-Net: Convolutional Networks for Biomedical Image Segmentation

5. Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Medical Image Segmentation: Ground Truth Optimization through Evaluating Uncertainty in Expert Annotations;Mathematics;2023-09-02

2. Transformer-Based Annotation Bias-Aware Medical Image Segmentation;Lecture Notes in Computer Science;2023

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