Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Autor: Mohammad Amin Abazari, Kaamran Raahemifar, Madjid Soltani, Farshad Moradi Kashkooli

Publikováno v: Cancers; Volume 14; Issue 11; Pages: 2786

No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aab81423bd3b9d51838b8d93227cbf44

Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection

Autor: Giancarlo Mauri, Leonardo Rundo, Changhee Han, Ryosuke Araki, Hideaki Hayashi, Yujiro Furukawa, Hideki Nakayama

Publikováno v: Neural Approaches to Dynamics of Signal Exchanges ISBN: 9789811389498
Neural Approaches to Dynamics of Signal Exchanges
Neural Approaches to Dynamics of Signal Exchanges, edited by Anna Esposito, Marcos Faundez-Zanuy Francesco Carlo Morabito, Eros Pasero, pp. 291–303. Basel: Springer Nature Switzerland, 2020
info:cnr-pdr/source/autori:Han C.; Rundo L.; Araki R.; Furukawa Y.; Mauri G.; Nakayama H.; Hayashi H./titolo:Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection/titolo_volume:Neural Approaches to Dynamics of Signal Exchanges/curatori_volume:Anna Esposito, Marcos Faundez-Zanuy Francesco Carlo Morabito, Eros Pasero,/editore: /anno:2020

Due to the lackof available annotated medical images, accurate computer-assisteddiagnosis requires intensive data augmentation (DA) techniques, such as geometric/intensity transformationsof original images; however, those transformed images intrinsic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::317173b3617820dc377f27f9f18e730d
http://hdl.handle.net/10281/255875

GAN-based synthetic brain MR image generation

Autor: Changhee Han, Giancarlo Mauri, Wataru Shimoda, Leonardo Rundo, Hideki Nakayama, Ryosuke Araki, Hideaki Hayashi, Yujiro Furukawa, Shinichi Muramatsu

Publikováno v: ISBI

In medical imaging, it remains a challenging and valuable goal how to generate realistic medical images completely different from the original ones; the obtained synthetic images would improve diagnostic reliability, allowing for data augmentation in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0945fe0f099cbecb013767d15f3089ef
http://hdl.handle.net/11386/4780070

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