Search Results for author: Liangqiong Qu

Found 16 papers, 6 papers with code

Residual Denoising Diffusion Models

1 code implementation25 Aug 2023 Jiawei Liu, Qiang Wang, Huijie Fan, Yinong Wang, Yandong Tang, Liangqiong Qu

In contrast to existing diffusion models (e. g., DDPM or DDIM) that focus solely on noise estimation, our RDDM predicts residuals to represent directional diffusion from the target domain to the input domain, while concurrently estimating noise to account for random perturbations in the diffusion process.

Denoising Image Generation +2

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

1 code implementation17 May 2022 Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.

Federated Learning Privacy Preserving +2

Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI

no code implementations9 May 2022 Yan-Ran, Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel Rubin, Heike E. Daldrup-Link

In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient.

Learning MRI Artifact Removal With Unpaired Data

no code implementations9 Oct 2021 Siyuan Liu, Kim-Han Thung, Liangqiong Qu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.

An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging

no code implementations18 Jul 2021 Liangqiong Qu, Niranjan Balachandar, Daniel L Rubin

In this paper, we investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew.

Federated Learning Privacy Preserving

SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging

1 code implementation6 Jul 2021 Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin

In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.

Binary Classification Federated Learning

Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging

no code implementations24 Jun 2021 Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel Rubin

Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients.

Image Generation Privacy Preserving

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

1 code implementation CVPR 2022 Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.

Federated Learning

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

no code implementations16 Nov 2020 Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Arun, Liangqiong Qu, Katharina Hoebel, Jay Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L Rubin, Jayashree Kalpathy-Cramer

Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types.

3D Hand Pose Tracking and Estimation Using Stereo Matching

no code implementations23 Oct 2016 Jiawei Zhang, Jianbo Jiao, Mingliang Chen, Liangqiong Qu, Xiaobin Xu, Qingxiong Yang

This paper demonstrates that the performance of the state-of-the art tracking/estimation algorithms can be maintained with most stereo matching algorithms on the proposed benchmark, as long as the hand segmentation is correct.

Hand Segmentation Pose Tracking +2

RGBD Salient Object Detection via Deep Fusion

no code implementations12 Jul 2016 Liangqiong Qu, Shengfeng He, Jiawei Zhang, Jiandong Tian, Yandong Tang, Qingxiong Yang

Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors.

object-detection RGB-D Salient Object Detection +3

Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image

no code implementations30 Jun 2014 Liangqiong Qu, Jiandong Tian, Zhi Han, Yandong Tang

In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image.

Shadow Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.