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Found 9 papers, 5 papers with code
CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation
The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge.
Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy.
KRNET: Image Denoising with Kernel Regulation Network
In this paper, we optimize CNN regularization capability by developing a kernel regulation module.
Image Restoration Using Deep Regulated Convolutional Networks
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 interest.
Ranked #1 on
Grayscale Image Denoising
on BSD200 sigma70
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion
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 based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests.
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
In this work, we explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn pixel-distribution from noisy data.
Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior
We explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn similar pixel-distribution features from noisy images.