4 citations · 6 across the 11 of their papers we have counts for
10 papers · 1 filter
RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
Shuang Zeng, Boxu Xie, Lei Zhu +6
Deep learning has greatly advanced medical image segmentation, but its success relies heavily on fully supervised learning, which requires dense annotations that are costly and tim…
Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
Zhengjian Yao, Jiakui Hu, Kaiwen Li +5
Blind face restoration remains a persistent challenge due to the inherent ill-posedness of reconstructing holistic structures from severely constrained observations. Current genera…
AdaTok: Adaptive Token Compression with Object-Aware Representations for Efficient Multimodal LLMs
Xinliang Zhang, Lei Zhu, Hangzhou He +5
Multimodal Large Language Models (MLLMs) have demonstrated substantial value in unified text-image understanding and reasoning, primarily by converting images into sequences of pat…
Inter- and Intra-image Refinement for Few Shot Segmentation
Ourui Fu, Hangzhou He, Kaiwen Li +5
Deep neural networks for semantic segmentation rely on large-scale annotated datasets, leading to an annotation bottleneck that motivates few shot semantic segmentation (FSS) which…
Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation
Shuang Zeng, Chee Hong Lee, Micky C Nnamdi +9
Retinal vessel segmentation is a vital early detection method for several severe ocular diseases. Despite significant progress in retinal vessel segmentation with the advancement o…
SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +2
Medical image segmentation is a critical yet challenging task, primarily due to the difficulty of obtaining extensive datasets of high-quality, expert-annotated images. Contrastive…
Exploiting Inherent Class Label: Towards Robust Scribble Supervised Semantic Segmentation
Xinliang Zhang, Lei Zhu, Shuang Zeng +5
Scribble-based weakly supervised semantic segmentation leverages only a few annotated pixels as labels to train a segmentation model, presenting significant potential for reducing…
V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer
Hangzhou He, Lei Zhu, Xinliang Zhang +3
Concept Bottleneck Models (CBMs) offer inherent interpretability by initially translating images into human-comprehensible concepts, followed by a linear combination of these conce…
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation
Qian Chen, Lei Zhu, Hangzhou He +4
The primary goal of continual learning (CL) task in medical image segmentation field is to solve the "catastrophic forgetting" problem, where the model totally forgets previously l…
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +10
Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learni…