Structuring Variability in Human Gait Datasets: A Covariate-Centered Taxonomy and Systematic Review of Image- and Depth-Based Collections [0.03%]
基于图像和深度数据的步态数据集的协变量中心分类法及系统性综述
João Ferreira Nunes,Pedro Miguel Moreira,João Manuel R S Tavares
João Ferreira Nunes
Human gait datasets play a central role in the development and evaluation of computer vision models. However, the current dataset landscape remains highly heterogeneous, with inconsistent reporting of acquisition conditions, user variabilit...
Decadal Changes in Institutional Diagnostic Reference Levels for X-Ray Angiography: A Retrospective Comparative Study [0.03%]
十年间介入放射学机构参考水平的回顾性比较研究
Ioannis Antonakos,Emmanouil Anousis,Tatiana Roko et al.
Ioannis Antonakos et al.
Angiography is a key imaging modality for the diagnosis and treatment of vascular diseases, and the growing sophistication of interventional procedures has heightened the need for radiation dose optimization. Diagnostic Reference Levels (DR...
LABFNet: A Restoration Network Guided by the LAB Colour Space and Frequency-Domain Constraints [0.03%]
受LAB颜色空间和频域约束引导的图像恢复网络LABFNet
Yaqian Zhang,Guanjun Wang,Quan Zhang et al.
Yaqian Zhang et al.
In the restoration of mural images with rich colour information and complex texture structures, existing techniques typically extract the spatial-domain features in the Red-Green-Blue (RGB) colour space. However, the three RGB channels are ...
Longitudinal CT Scanning for Explainable Early Detection of Postharvest Disorders: The 'Braeburn' Browning Case [0.03%]
基于纵向CT扫描的可解释早期采后病害检测方法：“Braeburn”苹果褐变案例研究
Dirk Elias Schut,Rachael Maree Wood,Rob Schouten et al.
Dirk Elias Schut et al.
This study presents two workflows for leveraging longitudinal computed tomography (CT) datasets when developing deep learning-based detection systems for gradually developing postharvest disorders. Workflow 1 (Longitudinal Benchmarking) ben...
Application of Machine Learning for Mean Glandular Dose Prediction Utilizing DICOM Mammography Images [0.03%]
基于DICOM乳腺X射线影像的ML平均剂量预测应用研究
Ali A A Alghamdi
Ali A A Alghamdi
The growing demand for raw and processed scientific data has encouraged many researchers and research institutions to adopt an open-source data policy. At present, data accessibility is of paramount importance due to the growing demand for ...
Dynamic Convolution Enhanced Attention Network for Pulmonary Nodule Detection [0.03%]
用于肺结节检测的动态卷积增强注意力网络
Shengqun Zhang,Annie Anak Joseph,Kho Lee Chin
Shengqun Zhang
Pulmonary nodules are circular or irregular lesions visible on chest computed tomography (CT), and their early detection is critical for lung cancer screening. Deep learning detection algorithms have been widely adopted for pulmonary nodule...
Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder [0.03%]
自闭症谱系障碍的多模态神经影像分类的对比学习与迁移学习方法研究
Raja Vavekanand,Ganesh Kumar,Muhammad Moazzam Jawaid et al.
Raja Vavekanand et al.
Autism Spectrum Disorder (ASD) assessment remains challenging because behavioural instruments are partly observer-dependent and neuroimaging data are heterogeneous. This paper presents FAA (Fuse After Aligned), which is a multimodal classif...
A Simplified CT Score for Thrombus Burden in Acute Pulmonary Embolism: Clinical Correlation and Reproducibility [0.03%]
急性肺栓塞血栓负荷的简化CT评分及其临床意义和可重复性
Ignacio Díaz-Lorenzo,Rio Jorge Aguilar Torres,Paloma Caballero Sanchez-Robles et al.
Ignacio Díaz-Lorenzo et al.
(1) Objectives: In acute pulmonary embolism (PE), detailed thrombus burden scores are often complex and time-consuming, limiting their integration into urgent radiology reports. We evaluated a simplified modified Ghanima score (GmScore and ...
Microscopic Pollen Image Classification via Contour-Signal Representation, Wavelet Analysis, and CNN [0.03%]
基于轮廓信号表示和小波分析的花粉图像分类方法研究
Abror Shavkatovich Buriboev,Akhram Nishanov,Shuxrat Isroilov et al.
Abror Shavkatovich Buriboev et al.
Accurate classification of pollen grains in microscopic images remains challenging because of noise, structural variability, background complexity, weak texture, and intra-class similarity. To address these issues, this study proposes a hyb...
Efficient Object Detection in Compressed Domain by Exploiting Knowledge Distillation from Pixel Domain [0.03%]
利用像素域知识蒸馏的压缩域高效目标检测方法
Serhat Dikyar,Behcet Ugur Toreyin
Serhat Dikyar
The proliferation of high-definition video data necessitates highly efficient processing pipelines for real-time edge analytics. However, traditional object detection architectures rely exclusively on pixel-domain inputs, which renders the ...