362 citations · 917 across the 15 of their papers we have counts for
15 papers
Residual Denoising Diffusion Models
Jiawei Liu, Qiang Wang, Huijie Fan +3
We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and…
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu +4
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in mul…
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
Yan-Ran, Wang, Liangqiong Qu +10
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time co…
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
Rui Yan, Liangqiong Qu, Qingyue Wei +5
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 da…
Learning MRI Artifact Removal With Unpaired Data
Siyuan Liu, Kim-Han Thung, Liangqiong Qu +3
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly ba…
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, Daniel L Rubin
Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. However, its distributed nature o…
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
Miao Zhang, Liangqiong Qu, Praveer Singh +2
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different instit…
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, Miao Zhang +1
Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging…
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang +5
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each inst…
Addressing catastrophic forgetting for medical domain expansion
Sharut Gupta, Praveer Singh, Ken Chang +13
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Sharut Gupta, Praveer Singh, Ken Chang +9
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institutio…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
3D Hand Pose Tracking and Estimation Using Stereo Matching
Jiawei Zhang, Jianbo Jiao, Mingliang Chen +3
3D hand pose tracking/estimation will be very important in the next generation of human-computer interaction. Most of the currently available algorithms rely on low-cost active dep…
RGBD Salient Object Detection via Deep Fusion
Liangqiong Qu, Shengfeng He, Jiawei Zhang +3
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 compactn…
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
Liangqiong Qu, Jiandong Tian, Zhi Han +1
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. Different from state-of-the…