most citedAchieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization

5 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

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…

cs.CV2022

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…

cs.LG20223 cited

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…

cs.CV20225 cited

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…

eess.IV2022

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…