Impact of synthetic data on training a deep learning model for lesion detection and classification in contrast-enhanced mammography [0.03%]
合成数据对对比增强乳腺X线摄影中病灶检测与分类深度学习模型训练的影响研究
Astrid Van Camp,Henry C Woodruff,Lesley Cockmartin et al.
Astrid Van Camp et al.
Purpose: Predictive models for contrast-enhanced mammography often perform better at detecting and classifying enhancing masses than (non-enhancing) microcalcification clusters. We aim to investigate whether incorporating...
Using a limited field of view to improve training for pulmonary nodule detection on radiographs [0.03%]
利用有限视野改善放射图像肺结节检测训练效果
Samual K Zenger,Rishabh Agarwal,William F Auffermann
Samual K Zenger
Purpose: Perceptual error is a significant cause of medical errors in radiology. Given the amount of information in a medical image, an image interpreter may become distracted by information unrelated to their search patt...
Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging [0.03%]
早期到晚期融合对不完美注册的多模式磁共振图像胰腺分割的影响
Lucas W Remedios,Han Liu,Samuel W Remedios et al.
Lucas W Remedios et al.
Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could impro...
New Growth, New Opportunities [0.03%]
新成长 新机遇
Bennett A Landman
Bennett A Landman
JMI Editor-in-Chief Bennett Landman discusses special issues and offers a few thoughts on the use of AI-assisted writing. © 2025 Society of Photo-Optica...
MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan populations [0.03%]
MD-SA2：优化Segment Anything 2，用于撒哈拉以南人群的跨模态深度感知脑肿瘤分割
Benjamin Li,Kai Ding,Dimah Dera
Benjamin Li
Purpose: Machine learning algorithms are emerging as valuable aides for radiologists in medical image segmentation due to their accuracy and speed. However, existing approaches, including both conventional machine learnin...
MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs [0.03%]
基于胸部X光片预测COVID-19严重程度的AI挑战赛：MIDRC mRALE Mastermind 大奖赛
Samuel G Armato rd,Karen Drukker,Lubomir Hadjiiski et al.
Samuel G Armato rd et al.
Purpose: The Medical Imaging and Data Resource Center (MIDRC) mRALE Mastermind Grand Challenge fostered the development of artificial intelligence (AI) techniques for the automated assignment of mRALE (modified radiograph...
Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study [0.03%]
基于三维因果深度学习的神经图像生成降维方法评价研究
Erik Y Ohara,Vibujithan Vigneshwaran,Raissa Souza et al.
Erik Y Ohara et al.
Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and...
Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration [0.03%]
基于密集位移采样的多对比度健康胰腺CT解剖图谱
Yinchi Zhou,Ho Hin Lee,Yucheng Tang et al.
Yinchi Zhou et al.
Purpose: Diverse population demographics can lead to substantial variation in the human anatomy. Therefore, standard anatomical atlases are needed for interpreting organ-specific analyses. Among abdominal organs, the panc...
Comparative analysis of nnU-Net and Auto3Dseg for fat and fibroglandular tissue segmentation in MRI [0.03%]
基于MRI的脂肪和腺体纤维组织分割的nnU-Net和Auto3Dseg比较分析
Yasna Forghani,Rafaela Timóteo,Tiago Marques et al.
Yasna Forghani et al.
Purpose: Breast cancer, the most common cancer type among women worldwide, requires early detection and accurate diagnosis for improved treatment outcomes. Segmenting fat and fibroglandular tissue (FGT) in magnetic resona...
Correlation of objective image quality metrics with radiologists' diagnostic confidence depends on the clinical task performed [0.03%]
客观图像质量指标与放射科医生诊断信心之间的相关性取决于执行的临床任务
Michelle C Pryde,James Rioux,Adela Elena Cora et al.
Michelle C Pryde et al.
Purpose: Objective image quality metrics (IQMs) are widely used as outcome measures to assess acquisition and reconstruction strategies for diagnostic images. For nonpathological magnetic resonance (MR) images, these IQMs...