Evaluating Iterative Deep Learning as a Labeling-Efficient Strategy for Tubular Segmentation in Digital Nephropathology [0.03%]
评估迭代深度学习作为数字肾病理学管状分割的标签高效策略有效性
Borghild Tednes Larsen,Yvan Samir Belakebi-Joly,Maya Maya Barbosa Silva et al.
Borghild Tednes Larsen et al.
Chronic kidney disease (CKD) is a prevalent condition worldwide and a significant global health burden that is expected to increase in the coming decades. Morphological evaluation of renal tubules is critical for diagnosis and prognosis; ho...
DADWMorph: A Global-Local Collaborative Network for Deformable Medical Image Registration [0.03%]
DADWMorph：用于变形医学图像配准的全局局部协作网络
Yujie Wang,Yinjie Su,Jiajia Liu et al.
Yujie Wang et al.
Deformable image registration enables spatial alignment across sequential scans for longitudinal disease monitoring, multi-modal fusion in treatment planning, and atlas-based segmentation. Accurate registration supports automated workflows ...
MMTC-Net: Multimodal Temporal Cervical Network for HSIL+ Recognition in Precancer Screening [0.03%]
用于癌前病变筛查的多模态时间宫颈网络HSIL+识别中的MMTC-Net算法研究
Ling Yan,Qingyu Wang,Yi Guo et al.
Ling Yan et al.
Cervical precancer screening is essential for reducing disease-related mortality. In colposcopic practice, clinicians jointly assess dynamic acetic-acid image sequences, iodine-stained images, and structured clinical information when distin...
Dense-MoE vs Lite-MoE: A Gating-Weight-Aware Pruning Framework for Unpaired Multimodal Breast Cancer Diagnosis [0.03%]
稠密MoE与轻量级MoE：一种未配对多模态乳腺癌诊断的门控权重感知剪枝框架
Dilber Cetintas,Taner Tuncer,Gulhan Kilicarslan et al.
Dilber Cetintas et al.
This study proposes a unique Mixture-of-Experts (MoE)-based deep learning framework for the effective use of unpaired multimodal images in breast cancer diagnosis. Mammography (MG), ultrasonography (US), and magnetic resonance imaging (MRI)...
Systematic Evaluation of Autoencoder Architectures for Medical Image Reconstruction: A Comprehensive Study Across Six Variants and Multiple Medical Imaging Modalities [0.03%]
基于自编码器的医学图像重建体系结构的系统评估：六种变体和多种医学成像模式的全面研究
Abdelbasset Boukdir
Abdelbasset Boukdir
Autoencoder-based representational learning models have become extremely popular tools enabling the analysis of medical images, yet systematic comparisons across diverse medical imaging modalities remain limited. Understanding which autoenc...
A Minimal UDI-DICOM Mapping Profile and Validation Artifact for Medical-Device Imaging Workflows [0.03%]
医疗设备成像工作流程的最小UDI-DICOM映射配置文件和验证工件
Bin Zhang
Bin Zhang
Medical-device imaging workflows need a reproducible way to connect unique device identification (UDI) with Digital Imaging and Communications in Medicine (DICOM) equipment metadata and with external evidence such as installation-acceptance...
Correction: Evaluating Large Language Models for Turkish Emergency CT Impression Drafting: Quality, Critical Omissions, and Readability [0.03%]
纠正：评估土耳其紧急CT影像报告草稿的大规模语言模型：质量、关键遗漏和可读性
Halil Tekdemir,Esra Çıvgın,Şebnem Akpınar et al.
Halil Tekdemir et al.
Published Erratum
Journal of imaging informatics in medicine. 2026 May 26. DOI:10.1007/s10278-026-02020-z 2026
ColoXAI-RecomNet: Explainable Recommender Framework for Colorectal Cancer Classification Using Integrated CNN Ensemble and LIME Interpretability [0.03%]
结直肠癌分类的可解释推荐框架：使用集成CNN和LIME解释性的COLOXAI-RecomNet
Akella S Narasimha Raju,Ranjith Kumar Gatla,G Sucharitha et al.
Akella S Narasimha Raju et al.
The classification of colorectal disease based on colonoscopy images requires not only high predictive accuracy but also interpretable decision support. This study proposes a five-stage explainable framework for multi-class colorectal image...
Label-Free Lung MRI Segmentation via Misalignment-Aware Diffusion Translation [0.03%]
基于偏移感知扩散翻译的无标签肺部MRI分割方法
Nejung Rue,Gyeongdeok Jo,Inye Na et al.
Nejung Rue et al.
Lung magnetic resonance imaging (MRI) is an attractive radiation-free modality for functional lung assessment, yet automated segmentation remains challenging due to low signal-to-noise ratio and weak boundary contrast, severely limiting the...
Automatic Localization and Classification of Crohn's Disease Activity in Computed Tomography Enterography Images Using Deep Learning [0.03%]
基于深度学习的磁共振小肠造影图像克罗恩病自动定位及分类技术
Peipei Wang,Yu Liu,Yuanjun Wang
Peipei Wang
Rapid and accurate localization and activity grading of Crohn's disease (CD) lesions on computed tomography enterography (CTE) images enhance the diagnostic efficiency of radiologists. We developed a one-stage model (called CD-YOLO) based o...