Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study [0.03%]
基于多尺度CT的可解释深度学习预测早期NSCLC隐匿性淋巴结转移：一项多中心研究
Zikang Yan,Xiaojuan Deng,Jun Dang et al.
Zikang Yan et al.
Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment planning. This study aimed to develop and validate a CT-based three-dimensional (3D) deep lea...
BF2GAN: A Fused Federated Learning-Based Biometric Enabled Deep Learning Approach for Secure Medical Image Sharing in Cloud Environment [0.03%]
基于生物特征的融合联邦学习方法，用于云环境中安全的医学图像共享
Zeeshan Ahmed Mohammed,D Rajeshwari,Shanavaz Mohammed et al.
Zeeshan Ahmed Mohammed et al.
Medical images contain sensitive and private health information of patients, which is crucial to be safeguarded from unauthorized access. Various encryption schemes are used for improving security; however, they are subject to adversaries a...
Maroš Kollár,Andrea Vajsová,Wanda Benesova
Maroš Kollár
Deep learning methods in medical imaging often suffer from the limited availability of high-quality annotated data, especially for rare conditions. This data scarcity is largely due to the need for domain expertise and the time-consuming pr...
Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography [0.03%]
基于锥形束计算体层摄影的AI生成患者信息的多维度评估方法研究
Melisa Öçbe,Hülya Çerçi Akçay,Eda Sır et al.
Melisa Öçbe et al.
This study aimed to conduct a multidimensional evaluation of artificial intelligence (AI) chatbot-generated patient information regarding cone beam-computed tomography (CBCT) in dentistry, with specific focus on readability, informational q...
Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration [0.03%]
基于MRI的四种影像组学模型在腰椎间盘退变诊断中的应用
Yan Chen,Fan Wang,Li Yu et al.
Yan Chen et al.
Lumbar intervertebral disc degeneration (LIDD) is a leading cause of low back pain, with subtle and variable imaging features that challenge early diagnosis. This study aimed to develop and validate a rigorous MRI-based radiomics ensemble m...
Using Radiomic Features to Detect Anatomical Errors and Assess Deep Learning-Based Left Ventricle Segmentation in Cardiac MRI [0.03%]
基于放射组学特征的心脏磁共振左心室分割解剖错误的检测及深度学习评价
Matheus A O Ribeiro,Marco A Gutierrez,Fátima L S Nunes
Matheus A O Ribeiro
Segmentation of the left ventricle in cardiac magnetic resonance exams is critical for accurate diagnosis and plays a central role in computer-aided diagnosis systems. Among the various automatic methods proposed, deep learning-based approa...
ThyroFusion: A Multi-modal Deep Learning Framework Integrating Vision and Language for Thyroid Nodule Malignancy Risk Assessment [0.03%]
甲状腺结节恶性风险评估的多模态深度学习框架-ThyroFusion：融合视觉和语言
Tianhao Xiang,Zhenyuan Hu
Tianhao Xiang
Accurate differentiation between benign and malignant thyroid nodules remains challenging in clinical practice. Current deep learning approaches predominantly rely on single-modality analysis, failing to leverage complementary information f...
Semi-supervised Learning with Online Knowledge Distillation for Skin Lesion Classification [0.03%]
在线知识蒸馏的半监督学习在皮肤病变分类中的应用研究
Siyamalan Manivannan
Siyamalan Manivannan
Deep learning has emerged as a promising approach for skin lesion analysis. However, existing methods mostly rely on fully supervised learning, requiring extensive labeled data, which is challenging and costly to obtain. To alleviate this a...
Text-Guided Multi-stage Cross-perception Network for Medical Image Segmentation [0.03%]
文本引导的多阶段跨感知网络在医学图像分割中的应用
Gaoyu Chen,Haixia Pan,Yuhan Tian et al.
Gaoyu Chen et al.
Medical image segmentation plays a crucial role in clinical medicine, serving as a key tool for auxiliary diagnosis, treatment planning, and disease monitoring. However, traditional segmentation methods such as U-Net are often limited by we...
League of Radiologists-an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography [0.03%]
放射学家联盟：一种面向放射学教育的端到端AI框架：胸部X光检查中的初步实现
Hyunji Kim,Young-Tak Kim,Saul Langarica et al.
Hyunji Kim et al.
Traditional radiology education is constrained by a restricted apprenticeship model and a scarcity of datasets structured for building artificial intelligence (AI)-based radiology education systems. To address this problem, we developed a n...