TFKT V2: task-focused knowledge transfer from natural images for computed tomography perceptual image quality assessment [0.03%]
CT图像感知质量评估的以任务为导向的自然图像到CT图像的知识迁移方法研究_v2
Kazi Ramisa Rifa,Md Atik Ahamed,Jie Zhang et al.
Kazi Ramisa Rifa et al.
Purpose: The accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic reliability while minimizing radiation dose. Radiologists' evaluations are time-consuming and labor-intensive. ...
Improving annotation efficiency for fully labeling a breast mass segmentation dataset [0.03%]
提高乳腺肿块分割数据集完全标注的效率
Vaibhav Sharma,Alina Jade Barnett,Julia Yang et al.
Vaibhav Sharma et al.
Purpose: Breast cancer remains a leading cause of death for women. Screening programs are deployed to detect cancer at early stages. One current barrier identified by breast imaging researchers is a shortage of labeled im...
Convolutional variational auto-encoder and vision transformer hybrid approach for enhanced early Alzheimer's detection [0.03%]
基于卷积变分自编码器和视觉变换器的混合方法实现增强型早期阿尔茨海默病检测
Harshani Fonseka,Soheil Varastehpour,Masoud Shakiba et al.
Harshani Fonseka et al.
Purpose: Alzheimer's disease (AD) is becoming more prevalent among the elderly, with projections indicating that it will affect a significantly large population in the future. Regardless of substantial research efforts an...
Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity [0.03%]
基于计算机断层扫描影像和卷积神经网络的慢性阻塞性肺疾病的分类：不同类型模型输入图像和不同训练数据严重程度的对比研究
Sara Rezvanjou,Amir Moslemi,Samuel Peterson et al.
Sara Rezvanjou et al.
Purpose: Convolutional neural network (CNN)-based models using computed tomography images can classify chronic obstructive pulmonary disease (COPD) with high performance, but various input image types have been investigat...
DECE-Net: a dual-path encoder network with contour enhancement for pneumonia lesion segmentation [0.03%]
一种基于轮廓增强的双路径编码网络用于肺炎病灶分割
Tianyang Wang,Xiumei Li,Ruyu Liu et al.
Tianyang Wang et al.
Purpose: Early-stage pneumonia is not easily detected, leading to many patients missing the optimal treatment window. This is because segmenting lesion areas from CT images presents several challenges, including low-inten...
Dependence of observer task on conclusions drawn from in silico trials evaluating the performance of full-field digital mammography and digital breast tomosynthesis [0.03%]
观察者任务对仿真评估全字段数字乳腺摄影和数字乳房断层合成性能的结论的影响依赖性
Dan Li,Andrey Makeev,Stephen J Glick
Dan Li
Purpose: We aim to refine the task-based evaluation of full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) through in silico trials (ISTs). Previous ISTs mostly employ lesion detection tasks for t...
Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images [0.03%]
基于B模式和Nakagami图像的多模态深度学习乳腺肿瘤诊断方法研究
Sabiq Muhtadi,Caterina M Gallippi
Sabiq Muhtadi
Purpose: We propose and evaluate multimodal deep learning (DL) approaches that combine ultrasound (US) B-mode and Nakagami parametric images for breast tumor classification. It is hypothesized that integrating tissue brig...
Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks [0.03%]
基于自监督学习和Swin Transformer与卷积神经网络混合深度模型的乳腺癌筛查影像检测改进算法研究
Han Chen,Anne L Martel
Han Chen
Purpose: The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence systems to breast cancer diagnosis. Deep models for mammogram analysis a...
WS-SfMLearner: self-supervised monocular depth and ego-motion estimation on surgical videos with unknown camera parameters [0.03%]
基于未知相机参数的内窥镜手术视域自我监督单目深度和自运动估算研究（WS-SfMLearner）
Ange Lou,Jack Noble
Ange Lou
Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challenges...
Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography: a simulation study [0.03%]
利用人工智能和合成乳房X线摄影减少数字乳腺体层摄影筛查的工作量：一项模拟研究
Victor Dahlblom,Magnus Dustler,Sophia Zackrisson et al.
Victor Dahlblom et al.
Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated f...