VECTOR-QUANTIZED LATENT FLOWS FOR MEDICAL IMAGE SYNTHESIS AND OUT-OF-DISTRIBUTION DETECTION

被引：0

作者：

Khader, Firas ^{[1
]}

Mueller-Franzes, Gustav ^{[1
]}

Arasteh, Soroosh Tayebi ^{[1
]}

Han, Tianyu ^{[2
]}

Kather, Jakob Nikolas ^{[3
,4
,5
,6
]}

Stegmaier, Johannes ^{[7
]}

Nebelung, Sven ^{[1
]}

Truhn, Daniel ^{[1
]}

机构：

[1] Univ Hosp Aachen, Dept Diagnost & Intervent Radiol, Aachen, Germany

[2] Rhein Westfal TH Aachen, Phys Mol Imaging Syst, Expt Mol Imaging, Aachen, Germany

[3] Univ Leeds, Leeds Inst Med Res St Jamess, Pathol & Data Analyt, Leeds, W Yorkshire, England

[4] German Canc Res Ctr, German Canc Consortium DKTK, Heidelberg, Germany

[5] Univ Heidelberg Hosp, Natl Ctr Tumor Dis NCT, Med Oncol, Heidelberg, Germany

[6] Tech Univ Dresden, Med Fac Carl Gustav Carus, Else Kroener Fresenius Ctr Digital Hlth, Dresden, Germany

[7] Rhein Westfal TH Aachen, Inst Imaging & Comp Vis, Aachen, Germany

来源：

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI | 2023年

关键词：

Out-of-Distribution Detection; Generative Model; Medical Support;

D O I：

10.1109/ISBI53787.2023.10230460

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an innovative method that allows for simultaneous out-of-distribution detection and image generation by encoding images in the latent space of a vector-quantized autoencoder and using normalizing flow models. The technique is demonstrated on a medical dataset of knee radiographs and can be used to relieve clinical radiologists of tedious tasks of quality control while simultaneously guiding radiologic technologists to improved and standardized image quality during image acquisition.

引用

页数：5

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