56 citations · 155 across the 25 of their papers we have counts for
25 papers
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset
Yi Sheng, Junhuan Yang, Jinyang Li +6
As Artificial Intelligence (AI) increasingly integrates into our daily lives, fairness has emerged as a critical concern, particularly in medical AI, where datasets often reflect i…
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
Qingpeng Kong, Ching-Hao Chiu, Dewen Zeng +4
Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age.…
SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration
Jinming Zhuang, Zhuoping Yang, Shixin Ji +5
With the increase in the computation intensity of the chip, the mismatch between computation layer shapes and the available computation resource significantly limits the utilizatio…
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Sheng Li, Geng Yuan, Yue Dai +10
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-i…
REFRESH FPGAs: Sustainable FPGA Chiplet Architectures
Peipei Zhou, Jinming Zhuang, Stephen Cahoon +6
There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous com…
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
Ruiyang Qin, Jun Xia, Zhenge Jia +5
After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated conversation data to generate user-specific and perso…
Additional Positive Enables Better Representation Learning for Medical Images
Dewen Zeng, Yawen Wu, Xinrong Hu +3
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning a…
BiTrackGAN: Cascaded CycleGANs to Constraint Face Aging
Tsung-Han Kuo, Zhenge Jia, Tei-Wei Kuo +1
With the increased accuracy of modern computer vision technology, many access control systems are equipped with face recognition functions for faster identification. In order to ma…
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…
Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream
Yue Tang, Yawen Wu, Peipei Zhou +1
Detecting actions in videos have been widely applied in on-device applications. Practical on-device videos are always untrimmed with both action and background. It is desirable for…
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…
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…
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…
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…
EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization
Yue Tang, Xinyi Zhang, Peipei Zhou +1
Conventionally, DNN models are trained once in the cloud and deployed in edge devices such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time inference. However, the…
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled…
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning
Yawen Wu, Zhepeng Wang, Dewen Zeng +2
Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled data. Given the CL training data, generative models can…
Decentralized Unsupervised Learning of Visual Representations
Yawen Wu, Zhepeng Wang, Dewen Zeng +3
Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative lear…
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
Dewen Zeng, Yukun Ding, Haiyun Yuan +5
Automatic myocardial segmentation of contrast echocardiography has shown great potential in the quantification of myocardial perfusion parameters. Segmentation quality control is a…
PIRM: Processing In Racetrack Memories
Sebastien Ollivier, Stephen Longofono, Prayash Dutta +3
The growth in data needs of modern applications has created significant challenges for modern systems leading a "memory wall." Spintronic Domain Wall Memory (DWM), related to Spin-…
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng +2
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated it…