Year: 2019
Type: article
Abstract: Beta-amyloid plaques and tau neurofibrillary tangles (NFTs) are hallmark pathologies in Alzheimers disease. The amount of tau (known as the tau burden) is an important metric used to determine stages of Alzheimers disease. Recent work by Signaevsky, et. al. [1]has shown that convolutional neural networks can be used to determine accumulation of tangles in immunohistochemically stained tissue. The goal of this project is to compare the performance of several networks for the segmentation, quantification and classification of tau tangles and -amyloid plaques. The networks to be compared include U-Net, FCNet, SegNet and Mask-RCNN. The goal of this open-ended research is to evaluate the effectiveness of deep convolutional networks on classification of noisily labeled data.
Source: Digital WPI
Author: Alexander Jay Wurts
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3041117372