Booth Id:
ROBO042
Category:
Robotics and Intelligent Machines
Year:
2021
Finalist Names:
Lal, Sinan (School: Port Huron Northern High School)
Abstract:
Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical
images. Most DNNs developed for this purpose are based on some variation of the encoder-
decoder U-Net architecture. Here I show that Res-CR-Net, a novel type of fully
convolutional neural network, which was originally developed for the semantic segmentation
of microscopy images, and which does not adopt a U-Net architecture, is very effective at
segmenting the lung fields in chest X-rays from either healthy patients or patients with a
variety of lung pathologies.
Awards Won:
Third Award of $1,000