Standardized evaluation of automatic methods for perivascular spaces segmentation in MRI - MICCAI 2024 challenge results [0.03%]
基于MRI的脑小血管周围间隙自动分割方法的标准化评价——MICCAI 2024挑战赛结果
Yilei Wu,Yichi Zhang,Zijian Dong et al.
Yilei Wu et al.
Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative condition...
Causal gradient intervention for debiased and evidence-grounded medical visual question answering [0.03%]
因果梯度干预在去偏见和基于证据的医学视觉问答中的应用
Bing Liu,Ziyuan Yang,Lijun Liu et al.
Bing Liu et al.
Medical Visual Question Answering (Med-VQA) aims to answer clinically relevant questions based on medical images. However, existing methods often struggle to provide visual evidence that is consistent with the query and verifiable. Under sc...
LungRes80: Towards tangled surgical workflow recognition in video-assisted thoracoscopic surgery [0.03%]
肺部手术识别：视频辅助胸腔镜手术中复杂手术流程识别的探索
Diandian Guo,Shu Yang,Jialun Pei et al.
Diandian Guo et al.
Video-Assisted Thoracoscopic Surgery (VATS) is a minimally invasive procedure developed to remove specific lung segments for the treatment of early-stage lung diseases. The surgical procedure involves intricate vascular and bronchial anatom...
KongNet: A multi-headed deep learning model for detection and classification of nuclei in histopathology images [0.03%]
基于组织病理学图像的细胞核检测与分类的多头深度学习模型 KongNet
Jiaqi Lv,Esha Sadia Nasir,Kesi Xu et al.
Jiaqi Lv et al.
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel, cell-type...
Corrigendum to "Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping" [Medical Image Analysis 101 (2025) 103435] [0.03%]
“协同多任务学习和可解释的图像生物标志物在胶质瘤分级和分子亚型分类中的应用”的勘误表 [医学图像分析第101卷(2025年)103435号论文]
Qijian Chen,Lihui Wang,Zeyu Deng et al.
Qijian Chen et al.
Published Erratum
Medical image analysis. 2026 Jul 28:104238. DOI:10.1016/j.media.2026.104238 2026
Unsupervised adaptive sampling graph autoencoder for 3D surface encoding and mesh representation transfer [0.03%]
一种用于三维表面编码和网格表示转换的无监督自适应采样图自动编码器
Ines A Cruz-Guerrero,Joseph Nagel,Antonio R Porras
Ines A Cruz-Guerrero
Standardizing anatomical surface representations across heterogeneous datasets is critical for population-level modeling and downstream medical image analysis tasks. Although existing mesh autoencoders can achieve standardized latent surfac...
Welcome new doctor: Continual learning with expert consultation and autoregressive inference for whole slide image analysis [0.03%]
欢迎新医生：基于专家咨询和自回归推理的全片扫描图像分析中的终身学习方法
Doanh C Bui,Jin Tae Kwak
Doanh C Bui
Whole Slide Image (WSI) analysis, with its ability to reveal detailed tissue structures in magnified views, plays a crucial role in cancer diagnosis and prognosis. Due to their giga-sized nature, WSIs require substantial storage and computa...
Causality-inspired representation learning with spatiotemporal memory for polyp detection in endoscopic videos [0.03%]
受因果关系启发的结肠镜视频息肉检测的时空记忆表征学习方法
Zhuo Hu,Changjin Sun,Qi Zheng et al.
Zhuo Hu et al.
Early detection of colorectal polyps is crucial to reduce the morbidity and mortality associated with colorectal cancer. However, during endoscopy, continuous camera motion and the complex clinical environment often degrade key visual cues ...
Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning [0.03%]
基于西雅美自我监督学习的鲁棒且任务不可见的功能性fMRI表征学习
Jiyao Wang,Peiyu Duan,Nicha C Dvornek et al.
Jiyao Wang et al.
Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and the inherent subjectivity of psychiatric rating scales often lead to datasets with small ...
Patient-specific unsupervised neural reconstruction of cardiac blood flow from 4D flow MRI [0.03%]
基于4D流动磁共振的患者特异性无监督神经心脏血液流重建
Atharva Hans,Abhishek Singh,Yue-Hin Loke et al.
Atharva Hans et al.
Clinical 4D flow MRI can provide detailed measurements of cardiac blood flow, but current workflows rely on manual segmentation and post hoc velocity filtering, which introduce variability and reduce physical consistency. We introduce SMURF...