The main problem is the usage of cross-entropy (CE) loss. Although U-Net is a common network for medical image segmentation, it has issues dealing with class imbalance labels. Hence, it is imperative to program a method of OCT-based automatic retina layer segmentation.Ĭonvolutional neural networks (CNNs) have achieved state-of-the-art performance in a breadth of image segmentation tasks, and they have robust and nonlinear feature extraction capabilities. Segmentation of OCT images is a challenging task.
CROSS ENTROPY MANUAL
Because of noise and artifacts (e.g., eye motions, the vessel projection shadow), manual Images obtained with optical coherence tomography (OCT). Unlike current standard methods that are invasive and expensive for AD detection, the thickness of the retina layer can be noninvasively assessed using high-resolution Previous studies have reported that the retina shares similar anatomical and physiological features with the brain, so it can be used as a possible biomarker for AD diagnosis in clinical practice. Comparing theīaseline model with adding the guided-attention, our results demonstratedĪD is a progressive neurodegenerative disease that gradually declines memory and cognitive function. Performance, and guiding the spatial attention feature maps by binary masksįurther improves the performance in our proposed network.
CROSS ENTROPY SERIAL
MultiSDGAN with a serial attention module provides the most competitive Our ablation study results on the WVU-OCTĭata-set in five-fold cross-validation (5-CV) suggest that the proposed Mechanism by forcing an L-1 loss between a specifically designed binary maskĪnd the generated attention maps. Mutli-stage attention framework where we incorporated a guided attention
Moreover, we developed and evaluated a guided Powerful feature maps by capturing rich contextual relationships to improve This investigation, we aim to evaluate and compare various combinations ofĬhannel and spatial attention to the MultiSDGAN architecture to extract more
(MultiSDGAN) to translate OCT scans in high-resolution segmentation labels. Proposed the multi-stage multi-discriminatory generative adversarial network Region, and unfavorable imaging conditions. This work aims to segment the OCT images automatically however, it is aĬhallenging task due to various issues such as the speckle noise, small target Optical coherence tomography (OCT) is one of the non-invasive andĮasy-to-acquire biomarkers (the thickness of the retinal layers, which isĭetectable within OCT scans) being investigated to diagnose Alzheimer's disease
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