6 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…