Grating based laboratory 5.41 keV X-ray phase contrast microscopy with zone-plate: Design, instrumentation and validation [0.03%]
基于光栅的实验室5.41 keV X射线波带片相衬显微镜：设计、仪器和验证
Yuhang Tan,Jiecheng Yang,Jiongtao Zhu et al.
Yuhang Tan et al.
BackgroundIt has been demonstrated that grating interferometers can be utilized to enhance the X-ray imaging contrast of low-density and weakly absorbing objects.ObjectiveIn this study, the design, instrumentation and validation of a gratin...
DBT reconstruction based on 3D coordinate transformation of multi-angle projections [0.03%]
基于多角度投影三维坐标变换的DBT重建算法
Ping Chen,Chunyu Yu
Ping Chen
PurposeDigital Breast Tomosynthesis (DBT) is an emerging imaging technique for diagnosing breast disease. It reconstructs three-dimensional (3D) tomographic images from a limited number of projections within a restricted angular range. This...
EEA-UNet: An efficient element-wise adaptive attention-based network for abdominal multi-organ segmentation [0.03%]
一种高效的元素级自适应注意网络用于腹部多器官分割
Panpan Wu,Runpeng Guo,Ziping Zhao et al.
Panpan Wu et al.
Accurate X-ray computed tomography (CT) image segmentation of the abdominal organs is a key task in automated medical image analysis, with crucial applications in clinical decision-making, computer-aided diagnosis, and surgical planning. Ho...
AI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation [0.03%]
基于形态推理和人机交互验证的心脏扩大辅助筛查人工智能算法
Muhammad Masdar Mahasin,Agus Naba,Chomsin S Widodo et al.
Muhammad Masdar Mahasin et al.
Cardiomegaly screening via manual Cardiothoracic Ratio (CTR) measurement remains a clinical bottleneck, while contemporary deep learning solutions often suffer from algorithmic bloating. To address the need for resource-efficient and interp...
BPENet: Boundary perception enhancement network for retinal vessel and coronary angiogram segmentation [0.03%]
一种改进边界的网络模型用于视网膜血管和冠脉血管分割
Linfeng Kong,Yun Wu
Linfeng Kong
Vessel segmentation is crucial for assisting in the diagnosis and treatment of a range of diseases, such as retinal diseases and coronary artery diseases. However, current vessel segmentation methods often face the problem of poor vessel bo...
Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting [0.03%]
半监督YOLO-DEP的高分辨率X射线组件定位与计数方法
Zhixuan Xiao,Huahai Sun,Xu Tuo et al.
Zhixuan Xiao et al.
Accurate localization and counting of tiny electronic components in high-resolution X-ray images is a critical yet challenging task in nuclear science, radiation imaging, and industrial quality control. Traditional methods suffer from poor ...
Attention based multi-scale edge-aware segmentation and convolutional transformer framework for automated glaucoma detection from fundus images [0.03%]
一种基于注意力的多尺度边缘感知分割和卷积变换框架的自动化青光眼检测方法
C Moorthy,D Arulanantham,A Suresh Babu et al.
C Moorthy et al.
BackgroundGlaucoma is a leading cause of irreversible vision loss and is characterized by subtle structural changes in the optic disc and optic cup. However, existing automated detection systems often suffer from weak boundary delineation, ...
Improving the robustness of radiomic features to patient size variations in CBCT imaging for radiotherapy [0.03%]
基于CBCT放疗影像的患者体型变化对放射组学特征稳健性的影响提升方法研究
Farhang Bayat,Cem Altunbas
Farhang Bayat
BackgroundRadiomic feature extraction from cone-beam computed tomography (CBCT) images in radiotherapy has potential for predicting tumor control and treatment-related toxicity. However, the reliability of CBCT-based radiomics is limited by...
DH-OOD: A decoupled hybrid framework for robust skin lesion classification via semantic-structural fusion [0.03%]
基于语义结构融合的鲁棒皮肤病变分类的解耦混合框架
Benyuan He,Lei Yao,Ning Xue et al.
Benyuan He et al.
Real-world skin lesion classification faces three major challenges: severe class imbalance, high intra-class variability, and the need to reject out-of-distribution (OOD) samples. Conventional monolithic models often struggle to address the...
Development and evaluation of deep learning models for automatic coronary stenosis segmentation in X-ray angiography [0.03%]
用于X射线血管造影中冠状动脉狭窄自动分割的深度学习模型的发展与评估
Manli Zhang,Fangyan Li,Haijun Guo et al.
Manli Zhang et al.
Accurate segmentation of stenosis in X-ray angiography (XRA) images is crucial for the objective assessment of stenosis severity and subsequent treatment planning in coronary artery disease. Current clinical practice primarily relies on sub...