858 citations · 913 across the 16 of their papers we have counts for
16 papers
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's tr…
LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images
Nooshin Yousefzadeh, Charlie Tran, Adolfo Ramirez-Zamora +3
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential interventi…
DOMINO: Domain-aware Loss for Deep Learning Calibration
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially da…
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Hong Huang, Lan Zhang, Chaoyue Sun +3
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. Howeve…
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applicatio…
CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation
Peng Liu, Charlie T. Tran, Bin Kong +1
The diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one d…
Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration
Bhargav Sriram Siddani, S. Balachandar, Ruogu Fang
This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles…
Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
Peng Liu, Ruogu Fang
Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely th…
KRNET: Image Denoising with Kernel Regulation Network
Peng Liu, Xiaoxiao Zhou, Junyiyang Li +2
One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional n…
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion
Peng Liu, Ruogu Fang
With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray base…
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Peng Liu, Bin Kong, Zhongyu Li +2
Recently, deep neural networks have demonstrated comparable and even better performance with board-certified ophthalmologists in well-annotated datasets. However, the diversity of…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…
Image Restoration Using Deep Regulated Convolutional Networks
Peng Liu, Xiaoxiao Zhou, Yangjunyi Li +2
While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater intere…
Joint image edge reconstruction and its application in multi-contrast MRI
Yunmei Chen, Ruogu Fang, Xiaojing Ye
We propose a new joint image reconstruction method by recovering edge directly from observed data. More specifically, we reformulate joint image reconstruction with vectorial total…
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
Peng Liu, Ruogu Fang
In this work, we explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn pixel-distribution from noisy data. By increasing CNN's wi…
Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior
Peng Liu, Ruogu Fang
We explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn similar pixel-distribution features from noisy images. Many types of ima…