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Improving image labelling quality

delete2023-04-24
delete1
PRE
AI
T
Thomas G. Day *
J
John Simpson
R
Reza Razavi
B
Bernhard Kainz *
DOI:10.1038/s42256-023-00645-1delete
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Abstract

Abstract

En 中文
There is a continuing demand for high-quality, large-scale annotated datasets in medical imaging supported by machine learning. A new study investigates the importance of what type of instructions crowdsourced annotators receive.

Journal

Nature Machine Intelligence cover
Nature Machine Intelligence
IF:
23.9
Papers:
1.3K
Citations:
1.5W

Organization

No organization information available
Cited Papers

Cited Papers

Labelling instructions matter in biomedical image analysis
err2023-03-02
err35
errOAAI
errRadsch, Tim; Reinke, Annika; Weru, Vivienn; Tizabi, Minu D. D.; Schreck, Nicholas; Kavur, A. Emre; Pekdemir, Buenyamin; Ross, Tobias; Kopp-Schneider, Annette; Maier-Hein, Lena
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Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation
err2021-10-01
err56
errOAAI
errFeng, Ruiwei; Zheng, Xiangshang; Gao, Tianxiang; Chen, Jintai; Wang, Wenzhe; Chen, Danny Z.; Wu, Jian
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DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
err2017-02-01
err222
errOAAI
errRajchl, Martin; Lee, Matthew C. H.; Oktay, Ozan; Kamnitsas, Konstantinos; Passerat-Palmbach, Jonathan; Bai, Wenjia; Damodaram, Mellisa; Rutherford, Mary A.; Hajnal, Joseph V.; Kainz, Bernhard; Rueckert, Daniel
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Erroneous data: The Achilles' heel of AI and personalized medicine
err2022-07-22
err19
errOAAI
errKristiansen, Thomas Birk; Kristensen, Kent; Uffelmann, Jakob; Brandslund, Ivan
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