2 citations · 2 across the 16 of their papers we have counts for
15 papers · 1 filter
Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
Lichao Mou, Shilan Zhang, Chunlei Li +7
Large vision-language models (LVLMs) can be adapted to specialized medical imaging tasks via parameter-efficient fine-tuning approaches such as low-rank adaptation (LoRA), leading…
Towards Real-World Ultrasound Understanding: Large Vision-Language Models from Multi-Image Examinations with Long-Form Reports
Bingcong Yan, Chunlei Li, Jingliang Hu +3
Large vision-language models (LVLMs) have achieved strong performance across many medical imaging tasks, yet their application to ultrasound remains limited due to its inherent com…
On Revisiting Entropy for Identifying Mislabeled Images
Chunlei Li, Zixuan Zheng, Yilei Shi +5
Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge…
NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Suhang Yao +95
This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation
Yihong Yao, Chunlei Li, Canxuan Gang +4
Increasingly advanced data augmentation techniques have greatly aided clinical medical research, increasing data diversity and improving model generalization capabilities. Although…
Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration
Guanglu Dong, Chunlei Li, Chao Ren +4
Recently, significant breakthroughs have been made in all-in-one image restoration (AiOIR), which can handle multiple restoration tasks with a single model. However, existing metho…
Dual Distillation for Few-Shot Anomaly Detection
Le Dong, Qinzhong Tan, Chunlei Li +5
Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. Wh…
ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling
Yaxiong Chen, Qicong Wang, Chunlei Li +5
Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks,…
Multimodal Large Language Models for Medical Report Generation via Customized Prompt Tuning
Chunlei Li, Jingyang Hou, Yilei Shi +3
Medical report generation from imaging data remains a challenging task in clinical practice. While large language models (LLMs) show great promise in addressing this challenge, the…
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation
Yaxiong Chen, Chuang Du, Chunlei Li +5
Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists. While recent works have made progress, learning to align medi…
One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
Yaxiong Chen, Junjian Hu, Chunlei Li +6
Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availability and annotation. We introduce…
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning
Zixuan Zheng, Yilei Shi, Chunlei Li +3
Few-shot video object segmentation aims to reduce annotation costs; however, existing methods still require abundant dense frame annotations for training, which are scarce in the m…
Rethinking Cell Counting Methods: Decoupling Counting and Localization
Zixuan Zheng, Yilei Shi, Chunlei Li +3
Cell counting in microscopy images is vital in medicine and biology but extremely tedious and time-consuming to perform manually. While automated methods have advanced in recent ye…
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection
Chunlei Li, Yilei Shi, Jingliang Hu +2
Unsupervised anomaly detection using deep learning has garnered significant research attention due to its broad applicability, particularly in medical imaging where labeled anomalo…