Downstream-Aware Automated QC of Images and AI-Generated Segmentations [0.03%]
下游Aware的图像和AI生成分割的自动化质控
Abigail E Green,Mrinal K Dhar,Adriana V Gregory et al.
Abigail E Green et al.
Automated deep learning-based segmentation is increasingly used in medical imaging to enable rapid biomarker extraction. Manual quality control (QC) of segmentation outputs remains standard prior to downstream analysis. In autosomal dominan...
A Grok API-Based DICOM File-Attachment Structured Reporting Workflow for Silicone Breast Implant MRI: a Single-Center Proof-of-Concept Agreement Study [0.03%]
基于GROK API的硅胶乳房植入物MRI附件结构化报告工作流程的概念验证研究：单中心结果与协议分析
Diba Saygılı Öz,Mert Efe Sevim,Özge Tanişman et al.
Diba Saygılı Öz et al.
This study aims to evaluate a Grok application programming interface (API)-based file-attachment structured reporting workflow for silicone breast implant rupture detection on magnetic resonance imaging (MRI), using radiologist consensus as...
Interpretable Machine Learning Using Multimodal Ultrasound and MRI Features for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Dual-Center Retrospective Study [0.03%]
基于多模态超声和MRI特征的乳腺癌腋窝淋巴结转移预测：一项可解释机器学习的双中心回顾性研究
Yuxi Liu,Ming Zhang,Qing Xia et al.
Yuxi Liu et al.
The objective of the study is to develop and externally evaluate interpretable machine learning models integrating routinely reported ultrasound and MRI features for the preoperative prediction of axillary lymph node metastasis in patients ...
A Multiparametric MRI‑Based Deep Learning Model for Binary Triage of Lung Versus Non‑lung Brain Metastases: A Multicenter Proof‑of‑Concept Study [0.03%]
基于多参数磁共振影像的深度学习模型在肺和非肺来源脑转移瘤二元分类中的应用：一项多中心概念验证性研究
Mingzhen Wu,Siyu Sun,Mingjun Guo et al.
Mingzhen Wu et al.
Identifying the origin of brain metastases (BM) is essential for personalized treatment, particularly in patients with carcinoma of unknown primary or multiple primary neoplasms. We aimed to develop a multiparametric MRI-based three-dimensi...
Correction: COPD-TransNet: A Swin Transformer Network with Quantitative Emphysema Feature Fusion for COPD Detection and Staging from Opportunistic CT Scans [0.03%]
纠正：COPD-TransNet：一种融合定量肺气肿特征的Swin变换网络，用于从机会性CT扫描中检测和分期慢性阻塞性肺疾病
Ao Liu,Boyu Zhang,Weiyi Li et al.
Ao Liu et al.
Published Erratum
Journal of imaging informatics in medicine. 2026 Aug 18. DOI:10.1007/s10278-026-02200-x 2026
VesselVision-Net: Hybrid Global-Local Context Learning for Liver Vessel Segmentation in Hepatic Surgery Preplanning [0.03%]
基于混合全局局部语境学习的肝血管分割方法
Mahdiyeh Rahmani,Alimohammad Moradi,Niloofar Ayoobi Yazdi et al.
Mahdiyeh Rahmani et al.
Accurate segmentation of liver vessels on contrast-enhanced CT is essential for safe hepatic surgery preplanning. It is still challenging because vessels vary greatly in size and have complex branching, and small branches are difficult to d...
Argus-Net: Anatomy-Guided Hierarchical Learning for Pulmonary Vasculature Segmentation in Contrast and Non-Contrast CT [0.03%]
一种基于解剖的层次学习方法用于对比和非对比CT肺血管分割
Ahmet Özcan,Ebubekir Erdem
Ahmet Özcan
Automatic segmentation of the pulmonary vasculature from thoracic CT is essential for pulmonary embolism assessment, surgical planning, and disease monitoring. However, accurate segmentation remains challenging due to low contrast, anatomic...
Development and Real-World Evaluation of an NLP-Assisted Quality Control System for Radiology Reports [0.03%]
一种用于放射学报告的NLP辅助质量控制系统的发展和实际评价
Sha-Sha Zhao,Si-Rui Li,Ting Ge et al.
Sha-Sha Zhao et al.
Maintaining the quality and consistency of radiology reports has become increasingly challenging with the growing volume of imaging examinations. This study aimed to develop and evaluate an NLP-assisted quality control system for routine ra...
Context-Aware Data Augmentation for Detection, Segmentation, and Quantitative Analysis of Mitotic Figures in Melanoma Whole-Slide Images [0.03%]
上下文感知数据增强在黑色素瘤全片图像中有丝分裂图象的检测、分割及定量分析中的应用
Xiaodong An,Liu Liu,Mengmeng Liu et al.
Xiaodong An et al.
Mitotic figure counting is critical for evaluating melanoma proliferation and prognosis, but manual counting is subjective and poorly reproducible, and the sparse distribution of mitotic figures in whole-slide images leads to a scarcity of ...
Efficient Lesion Detection in Hysteroscopic Images Through Enhanced Attention Mechanisms and Directional Context Modeling [0.03%]
通过增强注意力机制和定向上下文建模有效检测宫腔镜图像中的病灶
Jie Zhang,XiaoLing Peng
Jie Zhang
Hysteroscopic lesion detection is clinically important for the early diagnosis of endometrial diseases. However, this task remains challenging due to the center-biased spatial distribution of lesions, anisotropic target morphology, and weak...