no code implementations • 5 Dec 2022 • Hong Huang, Lan Zhang, Chaoyue Sun, Ruogu Fang, Xiaoyong Yuan, Dapeng Wu
Neural network pruning has been a well-established compression technique to enable deep learning models on resource-constrained devices.
1 code implementation • 13 Sep 2022 • Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, Ruogu Fang
Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation.
1 code implementation • 5 Oct 2021 • Peng Liu, Charlie T. Tran, Bin Kong, Ruogu Fang
The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge.
2 code implementations • 6 Jan 2020 • Peng Liu, Ruogu Fang
In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy.
no code implementations • 20 Oct 2019 • Peng Liu, Xiaoxiao Zhou, Junyiyang Li, El Basha Mohammad D, Ruogu Fang
In this paper, we optimize CNN regularization capability by developing a kernel regulation module.
1 code implementation • 19 Oct 2019 • Peng Liu, Xiaoxiao Zhou, Junyi Yang, El Basha Mohammad D, Ruogu Fang
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
no code implementations • 18 Oct 2019 • 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 based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests.
2 code implementations • 16 Oct 2019 • Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang
Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.
no code implementations • 8 Oct 2019 • José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti. R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, Joonho Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
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.
1 code implementation • 28 Jul 2017 • 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.
2 code implementations • 17 Jul 2017 • 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.