OCT Image Segmentation Using Neural Architecture Search and SRGAN

Dehzangi, Omid; Gheshlaghi, Saba Heidari; Amireskandari, Annahita; Nasrabadi, Nasser M.; Rezai, Ali R.

doi:10.1109/icpr48806.2021.9412818

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the introduction of the SRGAN it has been used in many different applications [179,31,185]. In addition, there are works such as [88] that presents some improvements in the SRGAN structure, the new architecture is known as Super Resolution Channel Attention GAN (srcaGAN).…”

Section: Super Resolution Gan (Srgan)mentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

View full text Add to dashboard Cite

In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed.The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

show abstract

Section: Super Resolution Gan (Srgan)mentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Conditional GANs depict good performance translating data from one domain to another [9], [10], thus it is appropriate for semantic segmentation. In our previous works, we proposed a GAN-based domain translation and superresolution architecture that learns to increase the medical image resolution from low to high and learn to segment the retinal layers at the same time [11,12,1]. This particular type of GAN considers multiple stages of output from different layers of the network.…”

Section: Introductionmentioning

confidence: 99%

Superresolution and Segmentation of OCT Scans Using Multi-Stage Adversarial Guided Attention Training

Jeihouni

Dehzangi

Amireskandari

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is one of the noninvasive 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 segment the OCT images automatically; however, it is a challenging task due to various issues such as the speckle noise, small target region, and unfavorable imaging conditions. In our previous work, we have proposed the multi-stage & multi-discriminatory generative adversarial network (MultiSDGAN) [1] to translate OCT scans in highresolution segmentation labels. In this investigation, we aim to evaluate and compare various combinations of channel and spatial attention to the MultiSDGAN architecture to extract more powerful feature maps by capturing rich contextual relationships to improve segmentation performance. Moreover, we developed and evaluated a guided mutli-stage attention framework where we incorporated a guided attention mechanism by forcing an L-1 loss between a specifically designed binary mask and the generated attention maps. Our ablation study results on the WVU-OCT data-set in five-fold crossvalidation (5-CV) suggest that the proposed MultiSDGAN with a serial attention module provides the most competitive performance, and guiding the spatial attention feature maps by binary masks further improves the performance in our proposed network. Comparing the baseline model with adding the guided-attention, our results demonstrated relative improvements of 21.44% and 19.45% on the Dice coefficient and SSIM, respectively.

show abstract