Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection-Reference-Cited by-同舟云学术

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Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

Published:2024-04-02 Issue: Volume:8673 Page:44
ISSN:
Container-title:Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications
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Author:

Segawa Arata,Nemoto Mitsutaka,Kaida Hayato,Kimura Yuichi,Nagaoka Takashi,Mikami Katsuhiro,Yamada Takahiro,Hanaoka Kohei,Tsuchitani Tatsuya,Kitajima Kazuhiro,Ishii Kazunari

Publisher

SPIE

Reference6 articles.

1. Automatic detection of primary and metastatic lesions on cervicothoracic region and whole-body bone using a uniform machine-learnable approach for [18F]-FDG-PET/CT image analysis

2. Y. Xue, S. Chen, J. Qin et al. “Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey”, Contrast Media & Molecular Imaging 2017(9512370), 1–10, 2017.

3. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery;Schlegl,2017

4. Anomaly detection in chest 18F-FDG PET/CT by Bayesian deep learning

5. A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations;Roth,2014

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