Výsledky vyhledávání - "Dehzangi, Omid"

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GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans

Autor: Jeihouni, Paria, Dehzangi, Omid, Amireskandari, Annahita, Rezai, Ali, Nasrabadi, Nasser M.

In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal layers. OCT has been identified as a non-invasive and inexpensive modality

Externí odkaz: http://arxiv.org/abs/2206.13740

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Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training

Autor: Jeihouni, Paria, Dehzangi, Omid, Amireskandari, Annahita, Dabouei, Ali, Rezai, Ali, Nasrabadi, Nasser M.

Optical coherence tomography (OCT) is one of the non-invasive and easy-to-acquire biomarkers (the thickness of the retinal layers, which is detectable within OCT scans) being investigated to diagnose Alzheimer's disease (AD). This work aims to segmen

Externí odkaz: http://arxiv.org/abs/2206.05277

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Efficient OCT Image Segmentation Using Neural Architecture Search

Autor: Gheshlaghi, Saba Heidari, Dehzangi, Omid, Dabouei, Ali, Amireskandari, Annahita, Rezai, Ali, Nasrabadi, Nasser M

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal laye

Externí odkaz: http://arxiv.org/abs/2007.14790

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