Due to a production error, the figures included in the article were not listed in the correct order. Figure 3 was included as Figure 4, Figure 4 was included as Figure 3, Figure 5 was included as Figure 6, and Figure 6 was included as Figure 5.
The publisher apologizes for this mistake. The original version of this article has been updated.
Summary
Keywords
hepatocellular carcinoma, CT, deep learning, MRI, microvascular invasion
Citation
Frontiers Production Office (2022) Erratum: Can a proposed double branch multimodality–contribution–aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?. Front. Oncol. 12:1092191. doi: 10.3389/fonc.2022.1092191
Received
07 November 2022
Accepted
07 November 2022
Published
01 December 2022
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
12 - 2022
Updates
Copyright
© 2022 Frontiers Production Office.
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: Frontiers Production Office, production.office@frontiersin.org
This article was submitted to Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers, a section of the journal Frontiers in Oncology
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