3.2k citations · 6k across the 61 of their papers we have counts for
61 papers
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…
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…
SAM 3D for 3D Object Reconstruction from Remote Sensing Images
Junsheng Yao, Lichao Mou, Qingyu Li
Monocular 3D building reconstruction from remote sensing imagery is essential for scalable urban modeling, yet existing methods often require task-specific architectures and intens…
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,…
High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation
Le Dong, Jinghao Bian, Jingyang Hou +5
Medical image analysis faces significant challenges in data sharing due to privacy regulations and complex institutional protocols. Dataset distillation offers a solution to addres…
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…
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
Chunlei Li, Yilei Shi, Haoxi Hu +3
High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical trade-off between image quality a…
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Yaxiong Chen, Minghong Wei, Zixuan Zheng +5
Referring medical image segmentation targets delineating lesions indicated by textual descriptions. Aligning visual and textual cues is challenging due to their distinct data prope…
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…
Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models
Tingxiu Chen, Yilei Shi, Zixuan Zheng +4
Ultrasound video classification enables automated diagnosis and has emerged as an important research area. However, publicly available ultrasound video datasets remain scarce, hind…
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…
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Yaxiong Chen, Yujie Wang, Zixuan Zheng +5
Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace. Automated segmentation can help but requires large labeled datasets, which are scarce. Semi-su…
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…
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Jie Liu, Tiexin Qin, Hui Liu +5
In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this t…
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
Zhenghang Yuan, Zhitong Xiong, Lichao Mou +1
An in-depth comprehension of global land cover is essential in Earth observation, forming the foundation for a multitude of applications. Although remote sensing technology has adv…
A Deep Active Contour Model for Delineating Glacier Calving Fronts
Konrad Heidler, Lichao Mou, Erik Loebel +3
Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often bee…
RRSIS: Referring Remote Sensing Image Segmentation
Zhenghang Yuan, Lichao Mou, Yuansheng Hua +1
Localizing desired objects from remote sensing images is of great use in practical applications. Referring image segmentation, which aims at segmenting out the objects to which a g…