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تسجيل مستخدم جديدConvolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection
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Since the beginning of this decade, CNN has been a very successful tool in the field of Computer Vision tasks.The invention of CNN was inspired from neuroscience and it shares a lot of anatomical similarities with our visual system.Inspired by the anatomyof humanvisual system, wearguethat the existing U-Net architecture can be improvedin many ways. As human visual system uses attention mechanism, we have used attention concatenation in place of normalconcatenation.Although, CNN is purely feed-forward in nature but anatomical evidences show that our brain contains recurrent synapses and they often outnumber feed-forward and top-down connections. Thisfact inspiresus to userecurrent convolution connectionsin place of normalconvolution blocksin U-Net.Thispaper also addressesthe class imbalance issuein the field of medical image analysis. The paperresolvestheproblem of class imbalanceswith the help of state-of-the-art loss functions.Weargue thatourproposed architecturecan be trained end to end with a few training data and it outperforms the other variantsof U-Net.
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processed by an azimuthal filter to reduce directional reflectance bias due to variation of beam incidence angle. The peripapillary area of the map was divided into 160 superpixels. Average reflectance was the mean of superpixel reflectance. Low-reflectance superpixels were identified as those with NFL reflectance below the 5 percentile normative cutoff. Focal reflectance loss was measure by summing loss in low-reflectance superpixels. Results: Thirty-five normal, 30 pre-perimetric and 35 perimetric glaucoma participants were enrolled. Azimuthal filtering improved the repeatability of the normalized NFL reflectance, as measured by the pooled superpixel standard deviation (SD), from 0.73 to 0.57 dB (p<0.001, paired t-test) and reduced the population SD from 2.14 to 1.78 dB (p<0.001, t-test). Most glaucomatous reflectance maps showed characteristic patterns of contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic sensitivity than the best NFL thickness parameter (overall, inferior, or focal loss volume): 53% v. 23% (p=0.027) in PPG eyes and 100% v. 80% (p=0.023) in PG eyes, with the specificity fixed at 99%. Conclusions: Azimuthal filtering reduces the variability of NFL reflectance measurements. Focal NFL reflectance loss has excellent glaucoma diagnostic accuracy compared to the standard NFL thickness parameters. The reflectance map may be useful for localizing NFL defects.
Narwhal is one of the most mysterious marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of this species, where behavioral information can be collected from instr
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