Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Huynh, Bao Ngoc; Groendahl, Aurora Rosvoll; Tomic, Oliver; Liland, Kristian Hovde; Knudtsen, Ingerid Skjei; Hoebers, Frank; van Elmpt, Wouter; Malinen, Eirik; Dale, Einar; Futsaether, Cecilia Marie

Huynh, Bao Ngoc; Groendahl, Aurora Rosvoll; Tomic, Oliver; Liland, Kristian Hovde; Knudtsen, Ingerid Skjei; Hoebers, Frank; van Elmpt, Wouter; Malinen, Eirik; Dale, Einar; Futsaether, Cecilia Marie.

Afiliação

Huynh BN; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Groendahl AR; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Tomic O; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Liland KH; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Knudtsen IS; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Hoebers F; Department of Medical Physics, Oslo University Hospital, Oslo, Norway.
van Elmpt W; Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands.
Malinen E; GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands.
Dale E; Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands.
Futsaether CM; GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands.

Front Med (Lausanne) ; 11: 1421603, 2024.

Article em En | MEDLINE | ID: mdl-38813378

ABSTRACT

ABSTRACT

[This corrects the article DOI 10.3389/fmed.2023.1217037.].

Palavras-chave

artificial intelligence; deep learning; feature selection; head and neck cancer; interpretability; machine learning; outcome prediction; radiomics

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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Front Med (Lausanne) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Noruega

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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Front Med (Lausanne) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Noruega