Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning [0.03%]
基于深度学习的心腔分割和延迟增强磁共振图像心房自动分割及生物标志物提取技术
Fan Feng,James Kennelly,Zhaohan Xiong et al.
Fan Feng et al.
Atrial fibrillation (AF) is associated with progressive structural remodeling of the atria, including chamber dilation, fibrosis, and variations in atrial wall thickness (AWT). Late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI)...
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model [0.03%]
基于大语言模型的高效临床信息收集两阶段主动对话生成方法
Xueshen Li,Xinlong Hou,Nirumapa Ravi et al.
Xueshen Li et al.
Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and ot...
Ali Dabouei,Jishnu Parayil Shibu,Vibhu Dalal et al.
Ali Dabouei et al.
Laboratory automation integrates robotics, machine learning, and computer vision to enhance precision and efficiency while reducing costs. Despite its pivotal importance in fully automated experimentation, automatic monitoring of procedures...
Corrigendum to "Identification of gene regulatory networks associated with breast cancer patient survival using an interpretable deep neural network model" [Expert Syst. Appl. 262 (2025) 125632] [0.03%]
关于“使用可解释的深度神经网络模型识别与乳腺癌患者生存相关的基因调控网络”的勘误修正[Expert Syst. Appl. 262 (2025) 125632]
Xue Wang,Vivekananda Sarangi,Daniel P Wickland et al.
Xue Wang et al.
[This corrects the article PMC11643596.].
Published Erratum
Expert systems with applications. 2025 Apr 15:269:126605. DOI:10.1016/j.eswa.2025.126605 2025
Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence [0.03%]
使用可解释的人工智能发现新型肝细胞癌预后生物标志物
Elizabeth Gutierrez-Chakraborty,Debaditya Chakraborty,Debodipta Das et al.
Elizabeth Gutierrez-Chakraborty et al.
Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tool...
Identification of Gene Regulatory Networks Associated with Breast Cancer Patient Survival Using an Interpretable Deep Neural Network Model [0.03%]
利用可解释的深度神经网络模型识别与乳腺癌患者生存相关的基因调控网络
Xue Wang,Vivekananda Sarangi,Daniel P Wickland et al.
Xue Wang et al.
Artificial neural networks have recently gained significant attention in biomedical research. However, their utility in survival analysis still faces many challenges. In addition to designing models for high accuracy, it is essential to opt...
MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos [0.03%]
基于面部视频检测老年人轻度认知障碍的多分支分类器-MC-ViViT模型
Jian Sun,Hiroko H Dodge,Mohammad H Mahoor
Jian Sun
Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch C...
Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module [0.03%]
基于空间到时间注意力模块的面部视频访谈轻度认知障碍检测方法
Muath Alsuhaibani,Hiroko H Dodge,Mohammad H Mahoor
Muath Alsuhaibani
Early detection of Mild Cognitive Impairment (MCI) leads to early interventions to slow the progression from MCI into dementia. Deep Learning (DL) algorithms could help achieve early non-invasive and low-cost detection of MCI. This paper pr...
DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications [0.03%]
DeepSeeded：使用深层神经网络级联进行细菌生物被膜应用中密集细胞群的体积分割
Tanjin Taher Toma,Yibo Wang,Andreas Gahlmann et al.
Tanjin Taher Toma et al.
Accurate and automatic segmentation of individual cell instances in microscopy images is a vital step for quantifying the cellular attributes, which can subsequently lead to new discoveries in biomedical research. In recent years, data-driv...
Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population? [0.03%]
深度成人肺分割模型能否推广到儿科人群？
Sivaramakrishnan Rajaraman,Feng Yang,Ghada Zamzmi et al.
Sivaramakrishnan Rajaraman et al.
Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are train...