Energy-Efficient Usage of CT Scanners Through Mathematically Optimized Examination Scheduling [0.03%]
通过数学优化的检查预约提高CT扫描仪的能效
Martin Segeroth,Armin Nurkanović,Ashraya Indrakanti et al.
Martin Segeroth et al.
Medical imaging devices are large consumers of electricity. This study aims to estimate potential energy savings through optimized CT examination scheduling using an ideal (hypothetical) lower-bound and a clinically realistic model. Examina...
Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging [0.03%]
领域泛化缓解医学影像中的扫描仪引起的领域偏移
Dagoberto Pulido-Arias,Mason C Cleveland,Jay Patel et al.
Dagoberto Pulido-Arias et al.
Deep learning models for medical image analysis often fail in clinical deployment due to domain shift from varied acquisition hardware and protocols. We present a comprehensive evaluation of various domain generalization (DG) techniques to ...
A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy [0.03%]
二维乳腺超声影像组学系统综述和元分析：对病变特征描述及诊断准确性的影响
Omar Abd Al Mjed Allasasmeh,Areej H Al-Sarairah,Muath A Odeh Hurani et al.
Omar Abd Al Mjed Allasasmeh et al.
Radiomics applied to two-dimensional breast ultrasound has emerged as a potential noninvasive approach for differentiating benign from malignant breast lesions; however, its diagnostic accuracy and methodological reliability remain uncertai...
Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification [0.03%]
基于柯尔莫哥洛夫-阿诺德的局部-全局注意力的医学图像分类方法
Weichao Pan,Xu Wang,Chengze Lv et al.
Weichao Pan et al.
Medical image classification relies on both fine lesion details and broader anatomical context to support reliable clinical decisions. Existing attention mechanisms often capture only one of these aspects, resulting in unstable and incomple...
Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss [0.03%]
基于放射组学的特发性突发性感音神经性聋患耳和非患耳内耳CT影像人工智能辅助分析
Joochan Choi,Woogsang Sunwoo,Kwanggi Kim
Joochan Choi
We aimed to determine whether computed tomography (CT)-based radiomic features of the inner ear can distinguish the affected side from the contralateral normal-hearing side in patients with idiopathic sudden sensorineural hearing loss (ISSN...
High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates [0.03%]
高采纳，高期望：阿拉伯联合酋长国放射学家参与人工智能的横断面调查
Ayham Khan Ansari,Abdulrahman Hamshari,Muhammad Kamil Shahbaz et al.
Ayham Khan Ansari et al.
Artificial intelligence (AI) is poised to transform diagnostic radiology, yet data on its adoption and the perspectives of radiologists in the Middle East remain scarce. This study provides the first comprehensive analysis of AI engagement ...
Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning [0.03%]
基于稀疏数据学习的快速MRI复杂值多尺度混合注意力网络
Yongchun Ma,Yuanzhen Tang,Zhaoyang Jin
Yongchun Ma
This study proposes a complex-valued multi-scale attention U-Net framework (SCMAU-Net) for accelerated MRI (magnetic resonance imaging) reconstruction from undersampled k-space data. The method employs a complex-valued difference transform ...
Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning [0.03%]
基于深度卷积和序列学习的Gd-EOB-DTPA增强肝MRI的自动相位和序列识别
Tomomi Takenaga,Shouhei Hanaoka,Yukihiro Nomura et al.
Tomomi Takenaga et al.
The purpose of this study is to develop and validate a deep learning model for automatic identification of acquisition sequences (including T1-weighted sequences with various dynamic contrast-enhanced phases and other auxiliary sequences) i...
Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible [0.03%]
基于超声的AI在预测乳腺癌激素受体状态中的应用：“数字活检”可能吗？
Beyza Nur Kuzan,Can Ilgin,Murat Emeç et al.
Beyza Nur Kuzan et al.
Accurate and rapid determination of tumor histopathological features and molecular subtypes is critical for breast cancer prognosis and treatment strategies. This study evaluates the feasibility of a "digital biopsy" approach that uses mach...
OpenDicomViewer: A Lightweight Open-Source DICOM Viewer for macOS Built with Swift [0.03%]
OpenDicomViewer：一个用Swift编写的轻量级开源DICOM图像浏览器应用程序
JoonNyung Heo,Daeseong Kim,Kunhee Kim et al.
JoonNyung Heo et al.
Digital Imaging and Communications in Medicine (DICOM) viewers are essential tools in clinical practice, yet established open-source macOS viewers such as Horos rely on legacy Objective-C and C++ source trees. In the public Horos repository...