858 citations · 913 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…