Rao D, Reymann M, Faley P, Massanes F, Gohn W, Vija AH (2021)
Publication Language: English
Publication Type: Conference contribution, Abstract of a poster
Publication year: 2021
Conference Proceedings Title: GAN Generated Model Observer for one Class Detection in SPECT Imaging
Event location: Online
DOI: 10.1117/12.2582113
Model observers are mathematical models used to perform a specific task, such as lesion detection. In this document, we will restrict ourselves to ideal model observers, which do not try to mimic human performance but try to perform perfect classification. However, we will not be following the usual definition of ideal model observer, which describes the model observer as a statistical classifier between two classes. Instead we will define a GAN network and train it to generate images from class 𝐻0, without lesions, and then use the discriminator of the GAN network as a model observer. Our method relies on pix2pix, which is a type of conditional GAN, the network is first trained to generate SPECT reconstructions-like data from the corresponding CT images. Later, the discriminator is used on simulated lesions to validate is usage as a classifier.
APA:
Rao, D., Reymann, M., Faley, P., Massanes, F., Gohn, W., & Vija, A.H. (2021, February). GAN Generated Model Observer for one Class Detection in SPECT Imaging. Poster presentation at SPIE, Online.
MLA:
Rao, Disha, et al. "GAN Generated Model Observer for one Class Detection in SPECT Imaging." Presented at SPIE, Online Ed. SPIE, 2021.
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