Exploring Uncertainty for Clinical Acceptability in Head and Neck Deep Learning-Based OAR Segmentation-Reference-Cited by-同舟云学术

Exploring Uncertainty for Clinical Acceptability in Head and Neck Deep Learning-Based OAR Segmentation

Published:2023-04-18 Issue: Volume: Page:
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Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Cubero L.¹,Serrano J.²,Castelli J.³,De Crevoisier R.³,Acosta O.³,Pascau J.¹

Affiliation:

1. Universidad Carlos III de Madrid,Departamento de Bioingeniería,Madrid,Spain

2. Clínica Universidad de Navarra,Departamento de Oncología Radioterápica,Madrid,Spain

3. Université Rennes,CLCC Eugène Marquis, Inserm, LTSI - UMR 1099,Rennes,France,F-35000

Publisher

IEEE

Link

Reference9 articles.

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4. Transformer-based out-of-distribution detection for clinically safe segmentation;graham;Proc Mach Learn Res - MIDL,2022

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3. Unsupervised Bias Discovery in Medical Image Segmentation;Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging;2023