Unsupervised Domain Adaptation for Semantic Segmentation of PET/CT Images

Mikolajczak, Jakub; Archit, Anwai; Nguyen, Anh Tien; Pape, Constantin; Hauschild, Anne-Christin

doi:10.3205/24gmds035

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JOURNAL ARTICLE

Unsupervised Domain Adaptation for Semantic Segmentation of PET/CT Images

Mikolajczak, Jakub Archit, Anwai Nguyen, Anh Tien Pape, Constantin Hauschild, Anne-Christin

Year: 2024 Journal: German Medical Science (German Research Foundation) Publisher: German Heart Research Foundation

DOI: 10.3205/24gmds035

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Abstract

Introduction: Cancer remains a predominant global health concern, with lung cancer alone responsible for 350 deaths daily in the U.S. While medical imaging (MI) and histological examinations are fundamental diagnostic tools, manual analysis proves time-intensive and costly. Deep learning (DL) offers [for full text, please go to the a.m. URL]

Keywords:

Segmentation Deep learning Domain (mathematical analysis) Domain adaptation Pattern recognition (psychology) Medical imaging Lung cancer

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Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Unsupervised Domain Adaptation for Semantic Segmentation of PET/CT Images

Abstract

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