Search Results for author: Ruogu Fang

Found 16 papers, 9 papers with code

Morphological Profiling for Drug Discovery in the Era of Deep Learning

no code implementations13 Dec 2023 Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra, Zhe Jiang, Ruogu Fang, Lina Cui, Yousong Ding, Tamer Kahveci, Jiang Bian, Chenglong Li, Hendrik Luesch, Yanjun Li

Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Cell Segmentation Drug Discovery +2

DOMINO: Domain-aware Loss for Deep Learning Calibration

1 code implementation10 Feb 2023 Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, Ruogu Fang

Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered.

Distributed Pruning Towards Tiny Neural Networks in Federated Learning

no code implementations5 Dec 2022 Hong Huang, Lan Zhang, Chaoyue Sun, Ruogu Fang, Xiaoyong Yuan, Dapeng Wu

To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices.

Federated Learning Network Pruning

DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

1 code implementation13 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.

Image Segmentation Medical Image Segmentation +2

CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

1 code implementation5 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.

Unsupervised Domain Adaptation

Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

2 code implementations6 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.


Image Restoration Using Deep Regulated Convolutional Networks

1 code implementation19 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.

Image Denoising Image Restoration +1

SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion

no code implementations18 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.

Computational Efficiency Image Denoising

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

2 code implementations16 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.

Unsupervised Domain Adaptation

Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising

1 code implementation28 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.

Image Denoising

Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior

2 code implementations17 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.

Image Denoising

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