Search Results for author:
Found 11 papers, 1 papers with code
TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research
To validate the dataset's utility, 5 competitive Deep Learning models for automatic kidney segmentation were benchmarked, yielding average DICE scores from 83. 2% to 89. 1% for CT, and 61. 9% to 79. 4% for US images.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Real-time regression analysis with deep convolutional neural networks
We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data.
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals.
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics.
Glitch Classification and Clustering for LIGO with Deep Transfer Learning
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise.
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors.
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals.
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
Gravitational wave astronomy has set in motion a scientific revolution.
