302 citations · 898 across the 13 of their papers we have counts for
13 papers
IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation
Donghao Zhou, Jingyu Lin, Guibao Shen +8
Recent visual generative models enable story generation with consistent characters from text, but human-centric story generation faces additional challenges, such as maintaining de…
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary
Quande Liu, Cheng Chen, Qi Dou +1
Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medica…
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Meirui Jiang, Hongzheng Yang, Xiaoxiao Li +3
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsol…
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Hongzheng Yang, Cheng Chen, Meirui Jiang +4
Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institu…
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
Cheng Chen, Quande Liu, Yueming Jin +2
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world s…
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
Quande Liu, Hongzheng Yang, Qi Dou +1
Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms o…
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Quande Liu, Cheng Chen, Jing Qin +2
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trai…
Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
Zhao Wang, Quande Liu, Qi Dou
The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, develop…
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
Quande Liu, Qi Dou, Pheng-Ann Heng
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tack…
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Luyang Luo, Lequan Yu, Hao Chen +4
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large dis…
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
Quande Liu, Lequan Yu, Luyang Luo +2
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the…
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
Quande Liu, Qi Dou, Lequan Yu +1
Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this ta…
Unpaired Multi-modal Segmentation via Knowledge Distillation
Qi Dou, Quande Liu, Pheng Ann Heng +1
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities…