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CORRECTION article

Front. Oncol., 23 August 2024
Sec. Cancer Imaging and Image-directed Interventions

Corrigendum: PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images

Cong Li,&#x;Cong Li1,2†Shuanlong Che&#x;Shuanlong Che3†Haotian GongHaotian Gong4Youde DingYoude Ding1Yizhou LuoYizhou Luo2Jianing XiJianing Xi2Ling Qi,*Ling Qi1,5*Guiying Zhang,*Guiying Zhang1,2*
  • 1The Affiliated Qingyuan Hospital (Qingyuan Peoples’s Hospital), Guangzhou Medical University, Qingyuan, China
  • 2School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
  • 3Department of Pathology, Guangzhou KingMed Center for Clinical Laboratory, Guangzhou, China
  • 4School of Health Management, Guangzhou Medical University, Guangzhou, China
  • 5Division of Gastroenterology, Institute of Digestive Disease, the Affiliated Qingyuan Hospital (Qingyuan Peoples’s Hospital), Guangzhou Medical University, Qingyuan, China

A Corrigendum on
PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images

By Li C, Che S, Gong H, Ding Y, Luo Y, Xi J, Qi L and Zhang G (2024). Front. Oncol. 14:1347123. doi: 10.3389/fonc.2024.1347123

In the published article, there was an error in affiliation 1. Instead of “Affiliated Qingyuan Hospital, The Sixth Clinical Medical School, Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, Guangdong, China”, it should be “the Affiliated Qingyuan Hospital (Qingyuan Peoples’s Hospital), Guangzhou Medical University, Qingyuan, China”.

In the published article, there was an error in affiliation 5. Instead of “Institute of Digestive Disease, the Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China”, it should be “Division of Gastroenterology, Institute of Digestive Disease, the Affiliated Qingyuan Hospital (Qingyuan Peoples’s Hospital), Guangzhou Medical University, Qingyuan, China”.

In the published article, there was an error regarding the affiliations for Cong Li. As well as having affiliation 1, they should also have 2.

In the published article, there was an error regarding the affiliations for Ling Qi. As well as having affiliation 5, they should also have 1.

In the published article, there was an error regarding the affiliations for Guiying Zhang. As well as having affiliation 1, they should also have 2.

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

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.

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. 14:1478598. doi: 10.3389/fonc.2024.1478598

Received: 10 August 2024; Accepted: 14 August 2024;
Published: 23 August 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

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, cWlsaW5nMTcxOEBnemhtdS5lZHUuY24=; Guiying Zhang, Z3VpeWluZ3poYW5nQGd6aG11LmVkdS5jbg==

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.