23 citations · 25 across the 2 of their papers we have counts for
6 papers
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Aakash Kaku, Sahana Upadhya, Narges Razavian
We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrasti…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
Weicheng Zhu, Narges Razavian
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of disea…
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang +6
Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturat…
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda +1
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural ne…