87 citations · 809 across the 93 of their papers we have counts for
26 papers · 1 filter
Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
Zhenge Jia, Yiyu Shi, Jingtong Hu +2
Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive…
Self-supervised On-device Federated Learning from Unlabeled Streams
Jiahe Shi, Yawen Wu, Dewen Zeng +3
The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unla…
Hybrid Gate-Pulse Model for Variational Quantum Algorithms
Zhiding Liang, Zhixin Song, Jinglei Cheng +8
Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces s…
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Yejia Zhang, Xinrong Hu, Nishchal Sapkota +2
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations. The idea is to make…
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
An Zeng, Chunbiao Wu, Meiping Huang +10
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomogr…
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Jinglei Cheng, Hanrui Wang, Zhiding Liang +3
Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of p…
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Hanrui Wang, Pengyu Liu, Jinglei Cheng +10
Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been rel…
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
Ruiyang Qin, Zhiding Liang, Jinglei Cheng +2
Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has rec…
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use de…
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization
Gelei Xu, Yawen Wu, Jingtong Hu +1
Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and i…
Distributed Contrastive Learning for Medical Image Segmentation
Yawen Wu, Dewen Zeng, Zhepeng Wang +2
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data an…
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms
Zhiding Liang, Jinglei Cheng, Hang Ren +8
Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are it…
Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory (CiM) architectures based on emerging non-volatile memory (NVM) devices have demonstrated great potential for deep neural network (DNN) acceleration thanks to t…
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation
Xinrong Hu, Corey Wang, Yiyu Shi
This work presents a novel framework CISFA (Contrastive Image synthesis and Self-supervised Feature Adaptation)that builds on image domain translation and unsupervised feature adap…
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation
Qing Lu, Xiaowei Xu, Shunjie Dong +4
Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions. To achieve fast and accurate…
On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a promising candidate for accelerating deep neural networks (DNNs) with high energy efficiency. However…
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
Yu Fu, Yanyan Huang, Yalin Wang +6
Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve…
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
Boyang Li, Qing Lu, Weiwen Jiang +2
In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation int…
Federated Contrastive Learning for Volumetric Medical Image Segmentation
Yawen Wu, Dewen Zeng, Zhepeng Wang +2
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data an…
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
Bingqian Lu, Zheyu Yan, Yiyu Shi +1
In view of the performance limitations of fully-decoupled designs for neural architectures and accelerators, hardware-software co-design has been emerging to fully reap the benefit…
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis
Yawen Wu, Dewen Zeng, Xiaowei Xu +2
Many works have shown that deep learning-based medical image classification models can exhibit bias toward certain demographic attributes like race, gender, and age. Existing bias…
Variational Quantum Pulse Learning
Zhiding Liang, Hanrui Wang, Jinglei Cheng +9
Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus…
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Yi Sheng, Junhuan Yang, Yawen Wu +5
Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emer…
SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy e…