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OCT and fluorescence image standardization for GAN-based enhancement

Riferimenti KRISTEN MARIKO MEIBURGER, MASSIMO SALVI

Gruppi di ricerca Biolab: Ingegneria Biomedica

Descrizione Generative adversarial networks (GANs) are a promising field of artificial intelligence for the enhancement and standardization of images from numerous modalities. Optical coherence tomography (OCT) and fluorescence microscopy are two imaging modalities that present a similarity between the acquired images: they both can tend to present objects of interest within the image that have an intensity level that is comparable to the background noise.

Thesis proposal:
The aim of the study is the implementation of semiautomatic methods for the standardization of these
images to enhance only the objects of interest while leaving the background noise untouched. The optimized images will be then used to train a GAN to generalize the semi-automatic algorithm.

Vedi anche  gans_octfluoro.pdf 


Scadenza validita proposta 01/04/2023      PROPONI LA TUA CANDIDATURA




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