Toward robust modeling of breast biomechanical compression: an extended study using graph neural networks [0.03%]
基于图神经网络的乳腺生物力学压缩鲁棒建模研究
Hadeel Awwad,Eloy García,Robert Martí
Hadeel Awwad
Purpose: Accurate simulation of breast tissue deformation is essential for reliable image registration between 3D imaging modalities and 2D mammograms, where compression significantly alters tissue geometry. Although fini...
In-depth look at the use of pixel variance and the noise power spectrum in digital mammography quality control [0.03%]
像素方差和数字乳腺摄影质量控制中的噪声功率谱的深入研究
Kristina Tri Wigati,Hilde Bosmans,Joke Binst et al.
Kristina Tri Wigati et al.
Purpose: X-ray detector noise decomposition and normalized noise power spectrum (NNPS) are two metrics proposed in the European Guidelines for the quality control (QC) of digital mammography (DM) systems. We aim to examin...
Nathan Meulenbroek,Laura Curiel,Adam Waspe et al.
Nathan Meulenbroek et al.
Purpose: Dynamic focusing of received ultrasound signals, or beamforming, is foundational for ultrasound imaging. Conventionally, it requires arrays of ultrasound sensors to estimate where sound came from using time-of-fl...
Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography [0.03%]
基于计算机断层扫描的糖尿病患者腹部分型：适用于不同体质指数人群的数据驱动模型
Lucas W Remedios,Chloe Cho,Trent M Schwartz et al.
Lucas W Remedios et al.
Purpose: Although elevated body mass index (BMI) is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that more detailed measurements of b...
Baowei Fei,Metin Nafi Gurcan,Yuankai Huo et al.
Baowei Fei et al.
Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis [0.03%]
锥束乳腺CT中软组织病变和微钙化的检测力：级联系统分析
Thomas Larsen,Hsin Wu Tseng,Jing-Tzyh Alan Chiang et al.
Thomas Larsen et al.
Purpose: We aim to investigate the performance of dedicated breast computed tomography (CT) for the detection of soft-tissue lesions and compare it to the detection of microcalcification clusters using cascaded systems an...
Application of thin-slice and accelerated T1-weighted GRE sequences in 1.5T abdominal magnetic resonance imaging using deep learning image reconstruction [0.03%]
基于深度学习图像重建的1.5T腹部磁共振成像快速T₁加权GRE序列的应用研究
Natalie S Joos,Saif Afat,Marcel Dominik Nickel et al.
Natalie S Joos et al.
Purpose: Deep-learning (DL)-based image reconstruction (DLR) is a key technique for reducing acquisition time (TA) and increasing morphologic resolution in abdominal magnetic resonance imaging (MRI). We aim to compare the...
Patch relevance estimation and multilabel augmentation for weakly supervised histopathology image classification [0.03%]
弱监督下组织病理图像分类的补丁相关性评估与多标签数据增强方法研究
Bulut Aygunes,Ramazan Gokberk Cinbis,Selim Aksoy
Bulut Aygunes
Purpose: Weakly supervised learning (WSL) is widely used for histopathological image analysis by modeling images as sets of fixed-size patches and utilizing image-level diagnoses as weak labels. However, in multiclass cla...
Benchmarking of deep learning methods for generic MRI multi-organ abdominal segmentation [0.03%]
深度学习在MRI多器官腹部分割中的基准研究
Deepa Krishnaswamy,Cosmin Ciausu,Steve Pieper et al.
Deepa Krishnaswamy et al.
Purpose: Recent advances in deep learning have led to robust automated tools for segmentation of abdominal computed tomography (CT). Meanwhile, segmentation of magnetic resonance imaging (MRI) is substantially more challe...
Multiscale attention network with structure guidance for colorectal polyp segmentation [0.03%]
一种基于结构导向的多尺度注意力网络用于结肠息肉分割
Yang Yang,Jie Gao,Lanling Zeng et al.
Yang Yang et al.
Purpose: Accurate segmentation and precise delineation of colorectal polyp structures are crucial for early clinical diagnosis and treatment planning. However, existing polyp segmentation techniques face significant chall...