In the published article, there was an error in affiliation 1. Instead of “School of Biomedical Engineering, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Guangzhou Medical University, Guangzhou, China”, it should be “Affiliated Qingyuan Hospital, The Sixth Clinical Medical School, Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, Guangdong, China”.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
Keywords
pathological images, blood vessel, deep learning, object detection, attention mechanism
Citation
Li C, Che S, Gong H, Ding Y, Luo Y, Xi J, Qi L and Zhang G (2024) Corrigendum: PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images. Front. Oncol. 14:1472621. doi: 10.3389/fonc.2024.1472621
Received
29 July 2024
Accepted
31 July 2024
Published
09 August 2024
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
14 - 2024
Updates
Copyright
© 2024 Li, Che, Gong, Ding, Luo, Xi, Qi and Zhang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ling Qi, qiling1718@gzhmu.edu.cn; Guiying Zhang, guiyingzhang@gzhmu.edu.cn
†These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.