Advanced Monte Carlo Modeling of Automatic Exposure Control Vulnerabilities: Patient Positioning and Metallic Artifact Effects on Emergency CAP CT Dose Distribution [0.03%]
先进的自动曝光控制漏洞蒙特卡洛建模：患者定位和金属伪影对急诊冠状动脉CT剂量分布的影响
Mounir Lahlali,Morad El Kafhali,Rajaa Sebihi et al.
Mounir Lahlali et al.
This study investigates the influence of arm positioning and metallic objects on radiation dose distribution during emergency CT scans, where time-critical workflows demand both speed and precision. Sub-optimal arm positioning and metallic ...
SBEM-UNet: A Semantic Boundary and Contour-Enhanced Framework for Semisupervised Medical Image Segmentation [0.03%]
一种语义边界和轮廓增强的半监督医学图像分割框架
Hongwei Zhang,Kaijun Yang,Meifeng Shi et al.
Hongwei Zhang et al.
In medical image segmentation, inherent boundary ambiguity, tissue overlap, and weak intensity gradients often produce blurred or discontinuous edges, posing persistent challenges to accurate anatomical delineation. Although deep learning a...
Risk-Stratified Use of Concurrent Computer-Aided Diagnosis (CAD) in Chest CT: Gains in Overall Sensitivity with Loss for CAD-Negative Nodules [0.03%]
基于胸部CT风险分层的计算机辅助诊断(CAD)联合应用：总体灵敏度增加但无CAD假阴性结节
Yoshinobu Ishiwata,Ryo Aoki,Keiichi Horie et al.
Yoshinobu Ishiwata et al.
This study quantifies how concurrent computer-aided detection/diagnosis (CAD) alters radiologists' performance in chest CT, emphasizing CAD-negative nodules and the trade-off between overall sensitivity and detection of unmarked lesions. We...
Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma [0.03%]
基于多参数MRI生境影像组学联合深度迁移学习预测分化型甲状腺癌侵袭性的一项研究：一项初步探索性研究
Xinyi Li,Yun Zeng,Hao Wang et al.
Xinyi Li et al.
The objective of the study is to develop and validate a multiparametric MRI (mpMRI)-based model that integrated with habitat-based radiomics, deep transfer learning (DTL), and quantitative parameters for the preoperative prediction of extra...
ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis [0.03%]
ARIADNE：一种用于可信冠状动脉造影分析的感知推理协同框架
Zhan Jin,Yu Luo,Yizhou Zhang et al.
Zhan Jin et al.
Conventional pixel-wise loss functions fail to enforce topological consistency in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling prefer...
Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction [0.03%]
基于MRI的颅内 meningioma 的术前影像组学研究
Ilies Djebbara,Ancuta Ioana Friismose,Bo Halle et al.
Ilies Djebbara et al.
Radiomic models for meningioma consistency prediction typically optimise discrimination while remaining opaque: They rarely clarify which features drive predictions, where discriminative patterns arise, or what they correspond to on MRI, li...
VMAM-NET: A Model Agnostic Meta-Learning Network for Rare De Novo Glioblastoma Diagnosis [0.03%]
一种用于罕见的新型胶质母细胞瘤诊断的与模型无关的元学习网络
Kuljeet Singh,Deepti Malhotra,Sidi Mohamed Sid&#x;El Moctar
Kuljeet Singh
The diagnosis of grade IV brain tumors, such as de novo glioblastoma, has recently attracted a lot of scientific interest in neuroimaging and deep learning. Glioblastoma, a very rare and highly aggressive brain tumor, poses considerable dia...
Uncertainty-Aware Super-Resolution for Mammography Phantoms using a Dropout-Enabled SwinIR [0.03%]
基于SwinIR的不确定性感知乳腺医学影像超分辨率重建方法
Yutaka Katayama,Shinsaku Hiura,Rie Tanaka et al.
Yutaka Katayama et al.
This study was aimed at presenting a framework integrating uncertainty quantification into the SwinIR super-resolution model for mammography, addressing the "black box" limitation that hinders clinical trust. Monte Carlo (MC) Dropout was in...
Assessing the Risk of Frequent Premature Ventricular Contractions in Patients with Ischemic Cardiomyopathy: Development and Validation of a Nomogram Model [0.03%]
缺血性心肌病患者室早风险评估列线图模型的建立及验证
Jiwei Sun,Anhong Yu,Jianjun Yan et al.
Jiwei Sun et al.
To develop and validate an imaging-based nomogram model for assessing the risk of frequent premature ventricular contractions (PVCs) in patients with ischemic cardiomyopathy. A total of 212 patients with ischemic cardiomyopathy were randoml...
Interpretable Deep Learning Radiomics Model for Preoperative Prediction of High-Grade Soft Tissue Sarcomas: A Multicenter MRI Study [0.03%]
基于多中心磁共振影像的术前预测高级别软组织肉瘤的可解释深度学习影像组学模型研究
Miaomiao Yang,Jiyang Jin,Rui Wen
Miaomiao Yang
This study aims to develop a preoperative fat-suppressed T2-weighted imaging (FS-T2WI)-based deep learning radiomics (DLR) model for predicting high-grade soft tissue sarcomas (STSs). 129 patients from the Cancer Imaging Archive (TCIA) data...