5 citations · 8 across the 21 of their papers we have counts for
15 papers · 1 filter
An Adaptor for Triggering Semi-Supervised Learning to Out-of-Box Serve Deep Image Clustering
Yue Duan, Lei Qi, Yinghuan Shi +1
Recently, some works integrate SSL techniques into deep clustering frameworks to enhance image clustering performance. However, they all need pretraining, clustering learning, or a…
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi +3
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled da…
Towards Perfection: Building Inter-component Mutual Correction for Retinex-based Low-light Image Enhancement
Luyang Cao, Han Xu, Jian Zhang +4
In low-light image enhancement, Retinex-based deep learning methods have garnered significant attention due to their exceptional interpretability. These methods decompose images in…
Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
Zihan Cheng, Jintao Guo, Jian Zhang +4
To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen ta…
Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists
You Wang, Zekun Li, Lei Qi +3
Despite the promising performance achieved by current semi-supervised models in segmenting individual medical targets, many of these models suffer a notable decrease in performance…
START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to enable models to generalize to unseen target domains by learning from multiple source domains. Existing DG methods primarily rely on convolutiona…
The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation
Muyang Qiu, Jian Zhang, Lei Qi +3
Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain genera…
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization
Jiajun Hu, Jian Zhang, Lei Qi +2
Domain generalization (DG) aims to avoid the performance degradation of the model when the distribution shift between the limited training data and unseen test data occurs. Recentl…
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Lei Qi +3
Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods…
SETA: Semantic-Aware Token Augmentation for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to enhance the model robustness against domain shifts without accessing target domains. A prevalent category of methods for DG is data augmentation,…
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Shumeng Li, Lei Qi, Qian Yu +3
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce th…
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
Na Wang, Lei Qi, Jintao Guo +2
Domain generalization (DG) intends to train a model on multiple source domains to ensure that it can generalize well to an arbitrary unseen target domain. The acquisition of domain…
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Jian Zhang, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising approach to improve model generalization in DG is the identification of flat minima. One typi…
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Zekun Li, Lei Qi, Yinghuan Shi +1
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled…
DomainAdaptor: A Novel Approach to Test-time Adaptation
Jian Zhang, Lei Qi, Yinghuan Shi +1
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificit…