- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Rapid tomographic reconstruction through GPU-based adaptive optics
摘要： Large telescopes have important challenges in the near future. Increasing the size of mirrors and sensors suppose not only a design issue, but also new computational techniques are needed to deal with the large amount of data. Adaptive Optics is an essential part of extremely large telescopes, and it uses reference stars and a tomographic reconstructor to compensate the aberrations introduced by the atmosphere during observation. The Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) is a tomographic reconstructor based on neural networks which has been used during on-sky observations. In this paper CARMEN will be implemented in two different neural network frameworks, which use a Graphics Processing Unit to improve their performance. To time the training and execution will provide results of which framework is faster for its implementation in a real telescope and will supply new tools to keep improving the reconstruction ability of CARMEN.
关键词： Adaptive Optics,Torch,Neural Networks,TensorFlow
更新于2025-09-23 15:23:52
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RAMS: Remote and automatic mammogram screening
摘要： About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
关键词： Faster R-CNN,SVM,Deep Learning,DDSM,Convolutional,TensorFlow,INbreast,Mammograms,Telemedicine,Artificial Neural Network
更新于2025-09-19 17:15:36