BCD
273 papers
A Little Is Enough: Circumventing Defenses For Distributed Learning
Distributed learning is central for large-scale training of deep-learning models. However, they are exposed to a security threat in which Byzantine participants can interrupt or control the...
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly
We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs. We develop two separate models to...
Artificial Benchmark for Community Detection with Outliers (ABCD+o)
The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates...
A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose...
Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
In this work, we aim at predicting children's fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health. The target...
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising,...
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain...
Accelerating Green Computing with Hybrid Asymmetric Multicore Architectures and Safe Parallelism
In this paper we present a novel strategy for accelerating green computing by utilizing and adopting the Hybrid Asymmetric Multicore Architectures (HAMA) model with Safe Parallelism. Most of the...