56 citations · 125 across the 25 of their papers we have counts for
25 papers
Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
Dewen Zeng, Xinrong Hu, Yu-Jen Chen +3
Weakly supervised semantic segmentation (WSSS) methods using class labels often rely on class activation maps (CAMs) to localize objects. However, traditional CAM-based methods str…
Enhancing 3D Transformer Segmentation Model for Medical Image with Token-level Representation Learning
Xinrong Hu, Dewen Zeng, Yawen Wu +2
In the field of medical images, although various works find Swin Transformer has promising effectiveness on pixelwise dense prediction, whether pre-training these models without us…
Contrastive Learning with Synthetic Positives
Dewen Zeng, Yawen Wu, Xinrong Hu +2
Contrastive learning with the nearest neighbor has proved to be one of the most efficient self-supervised learning (SSL) techniques by utilizing the similarity of multiple instance…
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.…
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures
Ruiyang Qin, Zheyu Yan, Dewen Zeng +8
Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters. Although such learning methods can be optimized…
Learning to Skip for Language Modeling
Dewen Zeng, Nan Du, Tao Wang +4
Overparameterized large-scale language models have impressive generalization performance of in-context few-shot learning. However, most language models allocate the same amount of…
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…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
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…
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…
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…
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…
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
Xinrong Hu, Dewen Zeng, Xiaowei Xu +1
The success of deep learning methods in medical image segmentation tasks heavily depends on a large amount of labeled data to supervise the training. On the other hand, the annotat…
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays
Dewen Zeng, John N. Kheir, Peng Zeng +1
Contrastive learning has been proved to be a promising technique for image-level representation learning from unlabeled data. Many existing works have demonstrated improved results…
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…
Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography
Dewen Zeng, Mingqi Li, Yukun Ding +7
Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many app…
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…
Positional Contrastive Learning for Volumetric Medical Image Segmentation
Dewen Zeng, Yawen Wu, Xinrong Hu +6
The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because o…
Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis
Yutian Chen, Xiaowei Xu, Dewen Zeng +6
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) s…
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Dewen Zeng, Weiwen Jiang, Tianchen Wang +6
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the…