most citedPrototype Knowledge Distillation for Medical Segmentation with Missing Modality

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV2024

Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction

Zhongsen Li, Wenxuan Chen, Shuai Wang +3

The inductive bias of the convolutional neural network (CNN) can be a strong prior for image restoration, which is known as the Deep Image Prior (DIP). Recently, DIP is utilized in…

eess.IV2023

Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

Shuai Wang, Zipei Yan, Daoan Zhang +4

Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis…

cs.CV2023

Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation

Shuai Wang, Daoan Zhang, Zipei Yan +2

Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential con…

cs.CV20232 cited

Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

Shuai Wang, Zipei Yan, Daoan Zhang +3

Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in…

cs.CV2023

Bootstrap The Original Latent: Learning a Private Model from a Black-box Model

Shuai Wang, Daoan Zhang, Jianguo Zhang +2

In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users t…