Diffusion-Guided Mask-Consistent Paired Mixing for Endoscopic Image Segmentation
School of Intelligent Engineering, Sun Yat-sen University (Shenzhen Campus)（中山大学智能工程学院）；Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院耳鼻喉科）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
Augmentation for dense prediction typically relies on either sample mixing or generative synthesis. Mixing improves robustness but misaligned masks yield soft label ambiguity. Diffusion synthesis increases apparent diversity but, when trained as common samples, overlooks the structural benefit of mask conditioning and introduces synthetic-real domain shift. We propose a paired, diffusion-guided paradigm that fuses the strengths of both. For each real image, a synthetic counterpart is generated under the same mask and the pair is used as a controllable input for Mask-Consistent Paired Mixing (MCPMix), which mixes only image appearance while supervision always uses the original hard mask. This produces a continuous family of intermediate samples that smoothly bridges synthetic and real appearances under shared geometry, enlarging diversity without compromising pixel-level semantics. To keep learning aligned with real data, Real-Anchored Learnable Annealing (RLA) adaptively adjusts the mixing strength and the loss weight of mixed samples over training, gradually re-anchoring optimization to real data and mitigating distributional bias. Across Kvasir-SEG, PICCOLO, CVC-ClinicDB, a private NPC-LES cohort, and ISIC 2017, the approach achieves state-of-the-art segmentation performance and consistent gains over baselines. The results show that combining label-preserving mixing with diffusion-driven diversity, together with adaptive re-anchoring, yields robust and generalizable endoscopic segmentation.
Registration-Grounded Spectral Fusion for Unregistered WLI/NBI Endoscopic Lesion Segmentation
The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Benchmarking Endoscopic Surgical Image Restoration and Beyond
The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；Southern Medical University（南方医科大学）；Xidian University（西安电子科技大学）；Wuhan University（武汉大学）
EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control
organization= Department of Electronic Engineering, The Chinese University of Hong Kong , state= Hong Kong SAR , country= China；organization= The Chinese University of Hong Kong Shenzhen Research Institute , city= Shen Zhen , state= Guangdong , country= China；organization= The First Affiliated Hospital of Wenzhou Medical University , city= Wenzhou , state= Zhejiang , country= China；organization= Department of General Surgery \& Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University , city= Guang Zhou , state= Guangdong , country= China；organization= Department of Otolaryngology-Head；Neck Surgery, Singapore General Hospital, Duke-NUS Medical School , country= Singapore；organization= Shenzhen Key Laboratory of Robotics Perception；the Department of Electronic；Electrical Engineering, Southern University of Science
CPKD: Clinical Prior Knowledge-Constrained Diffusion Models for Surgical Phase Recognition in Endoscopic Submucosal Dissection
School of Biomedical Engineering（生物医学工程学院）；Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, Renji Hospital（消化内科与肝病科、上海消化疾病研究所、国家消化疾病临床医学研究中心、仁济医院）；College of Media Engineering（媒体工程学院）；Aier Institute of Digital Ophthalmology and Visual Science（数字眼科与视觉科学研究院）
EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学第一附属医院）；Centre for Artificial Intelligence and Robotics, Chinese Academy of Sciences（中国科学院人工智能与机器人中心）；School of Engineering and Imaging Sciences, King’s College London（伦敦大学国王学院工程与影像科学学院）
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
Research institute for signals, systems and computational intelligence, sinc(i)（信号、系统与计算智能研究所（sinc(i)））；FICH-UNL（拉普拉塔国立大学工程学院）；CONICET（阿根廷国家科学与技术研究理事会）
VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark
University of Massachusetts at Amherst（马萨诸塞大学阿默斯特分校）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；Indian Institute of Technology Delhi Abu Dhabi（印度理工学院德里分校阿布扎比校区）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）
Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；Lenovo Research（联想研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang Normal University（浙江师范大学）
Asymmetric Paired-Annotation Learning for Multi-Structure ULF Pediatric Brain MRI Segmentation
VinUniversity；College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Hanoi University of Science and Technology（河内科技大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）；Carnegie Mellon University（卡内基梅隆大学）；Northwestern University（西北大学）；Center for Innovations in Health Sciences, VinUniversity（VinUniversity健康科学创新中心）
针对0.064 T ULF儿科脑部MRI分割难题，提出基于nnU-Net的非对称监督策略AURA，利用两类非等效标注训练，在16例数据上取得优于基线的分割性能。
SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection
Trauma Radiology AI Laboratory (TRAIL)（创伤放射AI实验室）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；University of Maryland School of Medicine（马里兰大学医学院）
FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On
School of Electronic Information Engineering, Beihang University, Beijing, China（北京航空航天大学电子信息工程学院）；Zhongguancun Academy, Beijing, China（中关村学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室）
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections
National Center for Tumor Diseases (NCT)（国家肿瘤中心）；Translational Surgical Oncology（转化外科肿瘤学）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（医学系和图林根大学技术大学卡尔·古斯塔夫·卡尔医院）；DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿大学技术大学医学系和德累斯顿癌症研究中心）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗斯多夫亥姆霍兹中心）；Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden（具有人类在环的触觉互联网中心，德累斯顿技术大学）；Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden（visceral、胸外科和血管外科，医学系和图林根大学技术大学卡尔·古斯塔夫·卡尔医院）；Weldon School of Biomedical Engineering, Purdue University（生物医学工程韦尔登学校，普渡大学）；Medical Oncology, NCT, University Hospital Heidelberg, Germany（医学肿瘤学，国家肿瘤中心，海德堡大学医院，德国）；Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden（医学I，医学系和图林根大学技术大学卡尔·古斯塔夫·卡尔医院）
