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CORRECTION article

Front. Oncol., 18 May 2023
Sec. Skin Cancer
Volume 13 - 2023 | https://doi.org/10.3389/fonc.2023.1201237

Corrigendum: Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections

This article is a correction to:

  1. Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections

    1. Read original article
Katharina Kriegsmann&#x;Katharina Kriegsmann1†Frithjof Lobers&#x;Frithjof Lobers2†Christiane ZgorzelskiChristiane Zgorzelski3Jrg Kriegsmann,Jörg Kriegsmann4,5Charlotte JanßenCharlotte Janßen6Rolf Rüdinger MelißRolf Rüdinger Meliß7Thomas MuleyThomas Muley8Ulrich SackUlrich Sack2Georg Steinbuss&#x;Georg Steinbuss1‡Mark Kriegsmann*&#x;Mark Kriegsmann3*‡
  • 1Department of Hematology, Oncology and Rheumatology, Heidelberg University, Heidelberg, Germany
  • 2Department of Clinical Immunology, Medical Faculty, University of Leipzig, Leipzig, Germany
  • 3Institute of Pathology, Heidelberg University, Heidelberg, Germany
  • 4MVZ Histology, Cytology and Molecular Diagnostics Trier, Trier, Germany
  • 5Proteopath Trier, Trier, Germany
  • 6Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany
  • 7Institute for Dermatopathology, Hannover, Germany
  • 8Translational Lung Research Centre (TLRC) Heidelberg, Member of the German Centre for Lung Research (DZL), Heidelberg, Germany

A Corrigendum on
Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

By Kriegsmann K, Lobers F, Zgorzelski C, Kriegsmann J, Janßen C, Meliß RR, Muley T, Sack U, Steinbuss G and Kriegsmann M (2022) Front. Oncol. 12:1022967. doi: 10.3389/fonc.2022.1022967

Incorrect Data Availability Statement

In the published article, there was an error in the Data Availability statement. The links provided for the dataset and the code of the study were incorrect. The Data Availability statement was displayed as “The datasets for this study can be found here: https://heidata.uni-heidelberg.de/privateurl.xhtml?token=366931ac-50a2-43f9-880f-88d63e07d493 and here: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=33948224. The code to conduct the analysis can be found here: https://heidata.uni-heidelberg.de/privateurl.xhtml?token=366931ac-50a2-43f9-880f-88d63e07d493.”

The correct Data Availability statement appears below.

Data availability statement

The datasets for this study can be found here: https://doi.org/10.11588/data/7QCR8S and here: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=33948224. The code to conduct the analysis can be found here: https://doi.org/10.11588/data/7QCR8S.

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.

Missing Citation

In the published article, 38. Katharina Kriegsmann; Fritjof Lobers; Christiane Zgorzelski; Jörg Kriegsmann; Rolf Rüdinger Meliß; Ulrich Sack; Georg Steinbuss; Mark Kriegsmann, 2022, “Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections [data]”, https://doi.org/10.11588/data/7QCR8S, heiDATA, V1, was not cited in the article. The citation has now been inserted in Methods, Patient data, Paragraph 1, and should read:

“Whole slides from patients with BCC (n = 93), SqCC (n = 100), naevi (n = 98) and melanoma (n = 87) were extracted from the archive of the Institute of Pathology, Heidelberg University, the MVZ for Histology, Cytology and Molecular Diagnostics Trier and the Institute for Dermatopathology Hannover. Diagnoses were made according to the World Health Organization (WHO) Classification of Skin Tumours (13). All slides with representative tumor regions were scanned using an automated slide scanner (Aperio AT2, Leica Biosystems, Nussloch, Germany) with 400 x magnification, as previously described (14). Image data were anonymized and are provided along with this manuscript (38). Moreover, an independent external dataset of melanoma whole slides was downloaded from the website of the Cancer Imaging Archive (CPTAC-CM) (15). After quality review 62 cases were included as an external test set, while 41 of these cases were melanoma and 21 were tumor-free skin. The analysis was approved by the local ethics committee of Heidelberg University.”

The citation has also been inserted in Methods, Hard- and software, Paragraph 1, and should read:

“For training we used a p3.2xlarge instance from Amazon Web Services with a single V100 GPU while for inference we used a Lenovo P1 Gen 2 laptop. Further we used the Scientific Data Storage (SDS) service from Heidelberg University. Training and inference were performed using a singularity container image based on the TensorFlow Docker container image. For random augmentation we used the respective function in the image python module. The code is available at (38)”.

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.

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: deep learning, pathology, artificial intelligence, dermatopathology, digital pathology, deep learning - artificial neural network

Citation: Kriegsmann K, Lobers F, Zgorzelski C, Kriegsmann J, Janßen C, Meliß RR, Muley T, Sack U, Steinbuss G and Kriegsmann M (2023) Corrigendum: Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections. Front. Oncol. 13:1201237. doi: 10.3389/fonc.2023.1201237

Received: 06 April 2023; Accepted: 12 April 2023;
Published: 18 May 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Kriegsmann, Lobers, Zgorzelski, Kriegsmann, Janßen, Meliß, Muley, Sack, Steinbuss and Kriegsmann. 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: Mark Kriegsmann, bWFyay5rcmllZ3NtYW5uQG1lZC51bmktaGVpZGVsYmVyZy5kZQ==

†These authors have contributed equally to this work and share first authorship

‡These authors have contributed equally to this work and share last authorship

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.